Modeling Biotic and Abiotic Drivers of Public Health Risk from West Nile Virus In

Home , Anautogeny, Anopheles barberi, Anopheles crucians, Anopheles punctipennis, Culex erraticus, Culex salinarius

Modeling Biotic and Abiotic Drivers of Public Health Risk from West Nile Virus in

Ohio, 2002-2006

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Paul Angelo Rosile, MPH

Graduate Program in Public Health

The Ohio State University

2014

Dissertation Committee:

Michael Bisesi, PhD, Advisor

Song Liang, PhD, co-Advisor

Jiyoung Lee, PhD

Ningchuan Xiao, PhD

Armando Hoet, DVM, PhD

Von Sigler, PhD

Paul Angelo Rosile, MPH

2014

Abstract

West Nile virus (WNV) disease in humans causes systemic febrile illness, meningoencephalitis, and death. The WNV, a reemerging pathogen, found its way to

New York City, United States of America (U.S.), from the Mediterranean region in

Europe in 1999 causing a countrywide epizootic and epidemic by 2003, and by 2012, leaving a reported 37,088 total human cases, 16,196 neuroinvasive cases, 1474 deaths, with an average case fatality rate of 10% in its wake. From 2002-2006, Ohio reported 669 human cases, 487 neuroinvasive cases, and 47 deaths, 5536 WNV positive mosquito pools, and 1328 WNV total positive dead birds.

This study used captured data from this time frame to address the gap in translational research between mosquito control theoreticians and practitioners for better understanding, preventing, and controlling WNV transmission hazards and risks to humans, by developing a practical predictive model to be used in their mosquito control programs. Time-delayed indices were constructed as time periods relative to the week mosquitoes were trapped (weeks before and during the trapping week) to reach this goal.

Temperature (T), weekly cumulative precipitation (CP), and the Palmer Index (PDI) informed these indices that estimated the temporal position of phases of the mosquito life cycle and the ecological conditions necessary for the development within these phases in

ii relation to the trapping week. Descriptive statistical tools were used to characterize temporal and spatial patterns of: 1) T, CP, and the PDI relative to documented WNV mosquito infection; and, 2) reported human WNV disease, WNV positive bird deaths, mosquito infection rates (IRs), and mosquito density (abundance), by week, year and

Ohio County, and within the broader context of the U.S.. Regression analyses were performed using these same indices as predictor variables with mosquito IRs as the outcome to determine the biological and meteorological drivers underlying WNV infections in mosquitoes, and using mosquito IRs as the predictor variable with human

WNV case onsets as the outcome. A mathematical model (MM) was developed, evaluated and calibrated at the state and county levels using independent datasets from different years, by integrating functions containing the statistically significant meteorological drivers of WNV disease transmission, which resulted from the regression analysis and literature parameter values, into differential equations in order to gain insight into the biological processes fundamental to increased WNV infection in mosquitoes.

iii

Dedication

This document is dedicated to all those lives touched by West Nile virus and other

vector-borne diseases.

Acknowledgments

I would like to thank my wife, Mary, and my twins, Paul Vinny and Michael, for their unwavering love, support, motivating memes and unselfishness during my doctoral program; my curriculum committee for requiring higher-level statistics as part of my coursework; my dissertation committee for their guidance and patience; the CPH

Graduate Studies Committee for their administrative support; and to the mosquito control and public health professionals in Ohio who contributed the data that ensured the success of this research. Special thanks are given to Drs. Bisesi, Liang, and Weghorst, for being my technical and inspirational advisors. To the dedicated College of Public Health staff:

Judy Dawson, Jennifer Wells, Erin Strawser, Dawn Williams, Kathy Renick, Susan Price, and Don Shymanski and the IS staff, which helped me in times of need. Special thanks to

Feng Zhang for her GIS maps and to Juan Peng, Xin Huang, Di Cao, and Jess Ramey for their critical review of my statistical methods. To all of the West Nile virus researchers who contributed to the scientific literature and to vector control practitioners, thank you for your dedication to the prevention and control of WNV disease and for being prepared to research and to confront all emerging and reemerging vector-borne diseases.

Vita

1972...... Hubbard High School

1976...... B.S. Biology, Heidelberg University

1977-1979………………………………….. Registered Sanitarian, Seneca County

General Health District

1977-1986…………………………………..Private sector employment in the food

service industry, chef/co-owner Angelo’s

Iron Gate Café, Tiffin, Ohio

1986-1990 ...... Registered Sanitarian, Franklin County

General Health District

1990-1999 ...... Assistant Health Commissioner,

Environmental Health Director, Delaware

County/City Combined General Health

District

1994...... M.P.H. The Ohio State University

1999-2012 ...... Assistant Health Commissioner,

Environmental Health Director, Franklin

County General Health District

Publications

Water Environment Research Foundation (WERF). (2012). Surveillance and

Investigation of the Illness Reported by Neighbors of Biosolids Land Application and

Other Soil Amendments, Pilot Testing. WERF and the International Water Association

(IWA) Water Intelligence Online © IWA Publishing 2012.

(Paul Rosile, PI, Song Liang, Tim Buckley, Kathleen Carr, and Jennifer Li)

Silva HP, Rosile PA. Community environmental health assessment - The Delaware city- county health department experience, phase I - Issues identification. Journal of

Environmental Health. 1999. 62(3): 9-15.

Fields of Study

Major Field: Public Health

vii

Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments...... v

Vita ...... vi

Publications . ……………………………………………………………………………. vii

Table of Contents………………………………………………………………………..viii

List of Tables ...... xiv

List of Figures ...... xvii

Chapter 1: Introduction ...... 1

1.1 Significance, rationale, and innovation ...... 1

1.2 Study framework and overall research objective ...... 7

1.3 Research questions ...... 8

1.3.1 Research question 1 ...... 8

1.3.2 Research question 2 ...... 9

1.3.3 Research question 3 ...... 9

1.4 Research hypotheses ...... 9

1.4.1 Research hypothesis 1 ...... 9

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1.4.2 Research hypothesis 2 ...... 10

1.4.3 Research hypothesis 3 ...... 11

1.5 Specific aims ...... 11

1.5.1 Specific aim 1 ...... 11

1.5.2 Specific aim 2 ...... 12

1.5.3 Specific aim 3 ...... 12

1.6 Organization of the dissertation ...... 13

1.7 Background ...... 14

1.7.1 The West Nile ribonucleic acid (RNA) virus ...... 14

1.7.2 Epidemiology of human WNV disease, 1937-2012 ...... 19

1.7.2.1 An emerging infectious disease: WNV from the Eastern to the Western

Hemisphere ...... 19

1.7.3 WNV transmission dynamics...... 28

1.7.3.1 The effect of temperature on mosquito biology and the transmission of

infection ...... 28

1.7.3.2 The effect of WNV titer levels on the transmission of infection between

vectors and avian hosts ...... 33

1.7.3.3 Avian host, pathogen, and vector transmission cycle dynamics ...... 34

1.7.4 Detection of WNV infection in mosquitoes ...... 45

1.7.4.1 Detection methods overview...... 45

1.7.4.2 Sensitivity studies of WNV detection methods ...... 49

1.7.5 Estimation of WNV IRs in mosquito populations ...... 52

Chapter 2: Environmental and ecological conditions in Ohio and the temporal and spatial characterization of patterns of WNV mosquito infection and human disease for period

2002-2006 ...... 69

2.1 Abstract ...... 69

2.2 Introduction ...... 70

2.3 Methods...... 75

2.3.1 Study area ...... 75

2.3.2 Human case onset data ...... 76

2.3.3 Mosquito IR data...... 76

2.3.4 Live and dead bird surveillance data...... 80

2.3.5 Meteorological data ...... 81

2.3.6 Mosquito control program data ...... 84

2.4 Results ...... 85

2.4.1 WNV in humans, 2002-2006 ...... 85

2.4.2 WNV in mosquitoes and humans, 2002-2006 ...... 89

2.4.3 WNV in birds ...... 101

2.4.4 WNV ecology...... 105

2.4.5 Mosquito control programs ...... 111

2.4.6 Characterization of the temporal relationship of mosquito density,

WNV positive bird deaths, mosquito IR, and human case onset dates ...... 112

2.5 Discussion ...... 113

2.6 Conclusion ...... 121

Chapter 3: An analysis of biotic and abiotic drivers associated with WNV mosquito infection and WNV human disease in Ohio, 2002-2006 ...... 122

3.1 Abstract……………………………………………………………………...... 122

3.2 Introduction………………………………………………………………...... 123

3.2.1Weather-based drivers of WNV transmission in mosquitoes from field

observations ...... 126

3.2.2 Weather-based drivers of WNV transmission in humans from field

observations ...... 129

3.3 Methods...... 139

3.3.1 Study area ...... 139

3.3.2 Human WNV case onset data, collection and management ...... 139

3.3.3 Mosquito WNV IR data, collection and management ...... 140

3.3.4 Meteorological data, collection and management...... 146

3.3.5 Analytical methods ...... 149

3.4 Results ...... 155

3.4.1 The relationship between WNV in humans and IR ...... 155

3.4.2 The relationship between WNV in mosquitoes and time-delayed indices ...... 156

3.5 Discussion ...... 161

3.6 Conclusion ...... 167

Chapter 4: A practical weather driven model of WNV transmission and human health risk in Ohio, 2002-2006 ...... 169

4.1 Abstract ...... 169

4.2 Introduction……………………………………………………………...... ….170

4.3 Methods...... 178

4.3.1 Mosquito WNV IR data, collection and management ...... 178

4.3.2. Live and dead bird host surveillance data ...... 184

4.3.3 Meteorological data, collection and management...... 186

4.3.4 Analytical methods ...... 190

4.4 Results ...... 197

4.5 Discussion ...... 216

4.6 Conclusion ...... 231

Chapter 5: Synthesis, public health implications, and future research ...... 233

References……………………………………………………………………...... 245

Appendix A: Demographic characteristics of persons with WNV disease ...... 259

Appendix B: Demographic characteristics by clinical syndrome ...... 260

Appendix C: WNV case definition and clinical present ...... 262

Appendix D: Ohio Christmas Bird Count locations ...... 267

Appendix E: Ohio first order weather stations ...... 269

Appendix F: Ohio day length regions ...... 271

Appendix G: Ohio weather districts ...... 273

Appendix H: Mosquito control program index survey ...... 275

Appendix I: Adult and larval control survey ...... 278

Appendix J: Ohio human WNV cases per county, 2002-2006 ...... 283

Appendix K: Mosquito species data, 2002-2006 ...... 285

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Appendix L: Other bird species data, 2002-2006...... 289

Appendix M: Acronym key ...... 291

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List of Tables

Table 1.1. Total U.S. human WNV cases, neuroinvasive cases, deaths, and case fatality rates per year,1999-2012 ...... 23

Table 2.1. Estimated time delays of the indices representing the mosquito life cycle and ecology which were constructed as time periods relative to the week mosquitoes were trapped (week +1 is the trapping week)..………………………………….…...... 83

Table 2.2. Total Ohio human WNV cases, neuroinvasive cases, deaths, number of mosquitoes tested, number of pools tested, number of pools positive for WNV, number of dead birds tested, and number of birds positive for WNV by year, 2002-2006 ...... 86

Table 2.3. Human (H) WNV cases and WNV positive mosquito (M) pools for Cuyahoga,

Franklin, Hamilton, Lorain, Lucas, and Montgomery Counties in Ohio by year, 2002-

2006...... 87

Table 2.4. Number of Culex spp. pools tested, number of positive pools, number (density) of Culex spp. collected, and aggregated minimum infection rate (MIR) of Culex spp. for

Ohio by year, 2002-2006 ...... 90

Table 2.5. Cumulative density (count) of all species, density of Culex species, and %

Culex spp., for Cuyahoga, Franklin, Hamilton, Lake, Lorain, Lucas, and Montgomery

Counties in Ohio, 2002-2006 ...... 91

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Table 2.6. Number of WNV positive American Crows, Blue Jays, and other species by year, Ohio, 2002-2006 ...... 102

Table 2.7. Percentage of Ohio mosquito control programs with index values between 0 and 13 by year ...... 112

Table 3.1. Total Ohio human WNV cases, neuroinvasive cases, and deaths by year, 2002-

2006...... 140

Table 3.2. Total of all mosquito species tested, number of pools tested, and positive pools by year for Ohio, 2001-2006 ...... 143

Table 3.3. Number of Culex spp. pools tested, number of positive pools, number (density) of Culex spp. collected, and aggregated minimum infection rate (MIR) of Culex spp. for

Ohio by year, 2002-2006 ...... 143

Table 3.4. Cumulative density (count) of all species, density of Culex species, and %

Culex spp., for Cuyahoga, Franklin, Hamilton, Lake, Lorain, Lucas, and Montgomery

Counties in Ohio, 2002-2006 ...... 144

Table 3.5. Estimated time delays of the indices representing the mosquito life cycle and ecology which were constructed as time periods relative to the week mosquitoes were trapped (week +1 is the trapping week) ...... 148

Table 3.6. Zero-truncated negative binomial mixed model regression coefficient estimates ...... 155

Table 3.7. Univariate regression coefficient estimates ...... 157

Table 3.8. Multivariate regression coefficient estimates ...... 160

Table 4.1. Total of all mosquito species tested, number of pools tested, and positive pools by year for Ohio, 2001-2006 ...... 181

Table 4.2. Number of Culex spp. pools tested, number of positive pools, number (density) of Culex spp. collected, and aggregated minimum infection rate (MIR) of Culex spp. for

Ohio by year, 2002-2006 ...... 181

Table 4.3. Number of WNV positive American Crows, Blue Jays, and other species by year, Ohio, 2002-2006 ...... 186

Table 4.4. Estimated time delays of the indices representing the mosquito life cycle and ecology which were constructed as time periods relative to the week mosquitoes were trapped (week +1 is the trapping week) ...... 189

Table 4.5. Model parameter descriptions, parameter values, mean and range literature parameter values, parameter units, model functions, and initial densities of each model component ...... 195

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List of Figures

Figure 1.1. Flow chart of the study framework including overarching goals and objectives, research questions, hypotheses, specific aims, methods, descriptive, analytical and mathematical studies ...... 8

Figure 1.2. Depiction of West Nile spherical virus constructed with Envelope (E), premembrane/membrane (prM/M), and capsid (C) proteins ...... 15

Figure 1.3. West Nile virion genome showing structural and non-structural proteins ...... 16

Figure 1.4. West Nile virion entering the cellular endosome, releasing the capsid and

RNA genome into the cellular cytoplasm, translating the cytoplasm using the host cell’s ribosomes into a poly-protein, cleaving the polyprotein to produce the three mature structural proteins and seven mature non-structural proteins, replicating the proteins into negative sense copies aided by RdRp which serve as copies for many positive sense infectious RNA genomes; then assembling mature virions at the membrane of the ER and moving through the cell’s secretory pathway to the acidic compartments of the Golgi bodies, cleaving the prM protein causing E proteins to undergo a structural transition of

60 trimers of prM-E heterodimers to 90 parallel E dimers, organizing in rafts of three and lying flat against the lipid layers, and completing the replicating cycle ...... 17

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Figure 1.5. West Nile Virus United States cumulative epidemic curve from 1999-2008

(A), annual disease incidence by year, 1999-2008 (B), cumulative incidence by county,

1999-2008 (C), and average incidence of neuroinvasive disease by age from 1999-2012

(D) ...... 24

Figure 1.6. WNV transmission cycle and the mosquito life cycle including proper site development for oviposition the eggs, larvae, and pupae phase (eggs to hatch) and adult phase; feeding on bird and dead-end hosts such as humans; through the gonotrophic cycle between blood meals and oviposition; and through the extrinsic incubation period of the virus, the time between the mosquito being infected to becoming infectious, which depending on temperature and the virus titer of bird hosts, may take as long as one or more gonotrophic cycles ...... 36

Figure 2.1. Minimum infection rate statistical outliers from selected Ohio counties, 2002

...... 79

Figure 2.2. A graphical representation of the estimated time delays of the indices representing the mosquito life cycle and ecology including oviposition (OVP), gonotrophic (GON), eggs to hatch (E_H), oviposition site development (OVPSD, preoviposition site development (POVPSD) and overwinter (OW), which were constructed as time periods relative to the week mosquitoes were trapped (TW)...... 84

xviii

Figure 2.4. Cumulative human cases by onset week for Cuyahoga (A), Franklin (B),

Hamilton (C), Lorain (D), Lucas (E), and Montgomery (F) Counties, 2002-2006 ...... 88

Figure 2.5. Cumulative human cases by onset week for Cuyahoga, Franklin, Hamilton,

Lorain, Lucas, and Montgomery Counties, 2002-2006, with expanded view of the number of cases between 0 and 9 per week ...... 89

Figure 2.6. Average infection rate (IR) by trap week and total human cases by onset week for Ohio, 2002 (A) and 2003 (B) ...... 92

Figure 2.7. Average mosquito density (actual average density divided by 10) and average infection rate (IR) by trap week, and total human cases by onset week for Ohio, 2002 and

2003, plus expanded view ...... 93

Figure 2.8. Mosquito density (actual density divided by 100) and average infection rate

(IR) by trap week, adulticiding by control week, and total human cases by onset week for

Butler (A) and Cuyahoga (B) Counties, 2002 ...... 94

Figure 2.9. Mosquito density (actual density divided by 100) and average infection rate

(IR) by trap week, adulticiding by control week, and human cases by onset week for

Franklin (A) and Hamilton (B) Counties, 2002 ...... 96

Figure 2.10. Mosquito density (actual density divided by 100) and average infection rate

(IR) by trap week, adulticiding by control week, and human cases by onset week for Lake

(A) and Lorain (B) Counties, 2002 ...... 97

Figure 2.11. Mosquito density (actual density divided by 200 for Cuyahoga County and actual density divided by 100 for Franklin County) and average infection rate (IR) by trap

xix week, adulticiding by control week, and human cases by onset week for Cuyahoga (A) and Franklin (B) Counties, 2003 ...... 98

Figure 2.12. Mosquito density (actual density divided by 300) and average infection rate

(IR) by trap week, adulticiding by control week, and human cases by onset week for

Lorain County, 2003 ...... 99

Figure 2.13. Mosquito density (actual density divided by 100) for Cuyahoga, Franklin,

Hamilton, Lake, Lorain, Mahoning, Montgomery, Summit, and Butler Counties in Ohio by trap week (A), and an expanded view of density between 0 and 2000 (B), 2002 ...... 100

Figure 2.14. Audubon Society Christmas bird count (CBC) crow and Blue Jay density by year for Ohio (A), Cuyahoga County (B), Franklin County (C), Cincinnati and Hamilton

County (D) Lorain County (E), Lucus County (F), and Montgomery County (G) with blue trend lines for Blue Jays and black trend lines for crows, 2001-2006 ...... 101

Figure 2.15. Cumulative number of WNV positive birds by week for Ohio, 2002-2006

(A), number of WNV positive birds by species and year for Ohio, 2002-2006 (B), number of WNV positive birds by week for Cuyahoga, Franklin, Hamilton, Lake, Lorain,

Lucas, Montgomery, and Stark Counties, 2002 (C), and the number of WNV positive birds by week for Allen, Green Licking, Madison, Medina, Mercer, Ottawa, Portage, and

Wood Counties, 2002 (D) ...... 103

Figure 2.16. Oviposition (OVP) (A), gonotrophic (GON) (B), and eggs-to-hatch (E_H)

...... 104

Figure 2.17. Oviposition (OVPSD) and preoviposition (POVPSD) site development (A) and overwinter (OW) (B) time-delayed indices informed with weekly mean temperature

(T); oviposition (OVP) (C) index informed with weekly mean cumulative precipitation

(CP), Ohio, 2002-2006 ...... 105

Figure 2.19. Overwinter time-delayed index (A) informed with weekly mean cumulative precipitation (CP); oviposition (OVPSD) and preoviposition (POVPSD) site development

(B) and overwinter (OW) (C) time-delayed indices informed with weekly Palmer

Drought Index (PDI), Ohio, 2002-2006 ...... 106

Figure 2.20. Oviposition (OVP) index and gonotrophic (GON) time-delayed index informed with weekly mean day length (DL), Ohio 2002-2006 (A); actual DL divided by

100 by week from the longest week to the shortest week of the year, Ohio 2002 (B); DL rate of change between weeks, Cleveland, Ohio, 2002 (B); expanded view of DL rate of change between weeks from 15 and 18 minutes between weeks, Cleveland, Ohio, 2002

(C); overwinter (OW), oviposition (OVPSD) and preoviposition (POVPSD) site development time delay indices informed with weekly Palmer Drought Index (PDI),

Ohio,2002(D)……………...... 108

xxi oviposition index and gonotrophic time-delayed index informed with weekly mean cumulative precipitation (CP), Ohio, 2002 (D) ...... 109

Figure 2.22. Eggs-to-hatch (E_H) (A), overwinter (OW), oviposition (OVPSD) and preoviposition (POVPSD) site development time-delayed indices (B) informed with weekly mean cumulative precipitation (CP), Ohio, 2002; overwinter index informed with weekly Palmer Drought Index (PDI) (C), weekly mean cumulative precipitation (D), and weekly mean temperature (T) (E), Ohio, 2002-2006 ...... 110

Figure 2.23. Integration of peak mosquito density by trap week, total WNV positive bird deaths by week of collection, average mosquito infection rate (IR) by trap week, and human case onset dates by week within one graphical illustration, Ohio, 2002 ...... 113

Figure 2.24. Time-delayed indices informed with weekly mean temperature (T), weekly cumulative precipitation (CP), day length (DL), and the Palmer Drought Index (PDI) estimated from the descriptive study to be associated with increases in IRs ...... 116

Figure 3.1. Minimum infection rate statistical outliers for selected Ohio counties, 2002

...... 147

Figure 3.2. A graphical representation of the estimated time-delays of the indices representing the mosquito life cycle and ecology including oviposition (OVP), gonotrophic (GON), eggs to hatch (E_H), oviposition site development (OVPSD, preoviposition site development (POVPSD) and overwinter (OW), which were constructed as time periods relative to the week mosquitoes were trapped...... 149

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Figure 3.3. Number of weeks (y-axis = 0-254) by number of cases per week (x = 0-41),

Ohio, 2002-2006...... 151

Figure 3.4. Number of weeks (y-axis = 0-51) by number of cases per week (x = 0-41),

Ohio, 2002-2006...... 151

Figure 3.5. Mosquito Infection Rate Density...... 152

Figure 3.6. Mosquito Log Infection Rate Density...... 152

Figure 3.7. Time-delayed indices informed with weekly mean temperature (T), weekly cumulative precipitation (CP), day length (DL), and the Palmer Drought Index (PDI) estimated from the statistical analysis to be significantly associated with increases in IRs

...... 163

Figure 4.1. Vector, pathogen, host, environment relationship ...... 171

Figure 4.2. The epidemiologic triad of agent, host and the environment ...... 171

Figure 4.3. Minimum infection rate statistical outliers for selected Ohio counties, 2002

...... 183

Figure 4.4. Average mosquito density (actual average density divided by 10) and average infection rate (IR) by trap week, and human cases by onset week for Ohio, 2002 and

2003, plus expanded view ...... 184

Figure 4.5. Audubon Society Christmas bird count (CBC) crow and Blue Jay density by year for Ohio (A), Cuyahoga County (B), Franklin County (C), Cincinnati and Hamilton

County (D) Lorain County (E), Lucus County (F), and Montgomery County (G) with blue trend lines for Blue Jays and black trend lines for crows, 2001-2006 ...... 185

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Figure 4.6. Cumulative number of WNV positive birds by week for Ohio, 2002-2006 (A), number of WNV positive birds by species and year for Ohio, 2002-2006 (B), number of

WNV positive birds by week for Cuyahoga, Franklin, Hamilton, Lake, Lorain, Lucas,

Montgomery, and Stark Counties, 2002 (C), and the number of WNV positive birds by week for Allen, Green Licking, Madison, Medina, Mercer, Ottawa, Portage, and Wood

Counties, 2002 (D) ...... 187

Figure 4.7. A graphical representation of the estimated time delays of the indices representing the mosquito life cycle and ecology including oviposition (OVP), gonotrophic (GON), eggs to hatch (E_H), oviposition site development (OVPSD, preoviposition site development (POVPSD) and overwinter (OW), which were constructed as time periods relative to the week mosquitoes were trapped (TW)...... 190

Figure 4.8. Mean weekly temperature for the gonotrophic (GON) index, Franklin County,

Ohio, 2002. Weeks 0-20 represents epidemiological weeks 20-40 ...... 191

Figure 4.9. Mathematical model framework with larval mosquito (LM), susceptible mosquito (SM), exposed mosquito (EM), infectious mosquito (IM), susceptible bird (SB), and infectious bird (IB), mu equals natural per capita death rate per week, omega equals percentage death or larvae or mosquitoes per control week, sigma equals virus incubation rate per capita/week in mosquito from infected (EM) to infectious (IM), gamma equals bite rate per capita/week, b1 equals virus transmission coefficient from bird to mosquito, b2 equals virus transmission coefficient from mosquito to bird, ml equals maturation % of larvae surviving to adults, lambda equals birth rate per capita/week ...... 192

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Figure 4.10. Bite rate as a function of temperature from the Franklin County, Ohio, 2002,

TGON index. Weeks 0-20 represents epidemiological weeks 20-40 ...... 196

Figure 4.11. Fitted bite rate curve as a function of temperature from the Franklin County,

Ohio, 2002, TGON index. Weeks 0-20 represents epidemiological weeks 20-40 ...... 196

Figure 4.12. WNV transmission model using parameters in Table 4.5 showing density of larval mosquitoes, susceptible mosquito, exposed mosquito, infectious mosquito, susceptible bird, and infectious bird, Ohio, 2002. Time (x-axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week

40...... 198

Figure 4.13. WNV transmission model using parameters in Table 4.5 with temperature and time variable bite rate function showing estimated density of larval mosquitoes, susceptible mosquito, exposed mosquito, infectious mosquito, susceptible bird, and infectious bird, Ohio, 2002. Time (x-axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 ...... 199

Figure 4.14. WNV transmission fitted model of Franklin County, Ohio, 2002, to actual mosquito infection rate (IR), using parameters in Table 4.5, except the gamma parameter was changed to 4.75 per capita/week, with mosquito IR density represented by ▀. IR is not to scale with model estimated densities and used to illustrate peaks only. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 ...... 200

Figure 4.15. WNV transmission fitted model of Franklin County, Ohio, 2003, to actual mosquito infection rate (IR), using parameters in Table 4.5, except the gamma parameter

xxv was changed to 4.75 per capita/week, with temperature and time variable bite rate function, with IR density represented by ▀. IR is not to scale with model estimated densities and used to illustrate peaks only. Time (x-axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40…...... 201

Figure 4.16. WNV transmission fitted model of Cuyahoga County, Ohio, 2002, to actual mosquito infection rate (IR), using parameters in Table 4.5, except the gamma parameter was changed to 4.75 per capita/week, with mosquito infection rate (IR) density represented by ▀. IR is not to scale with model estimated densities and used to illustrate peaks only. Time (x-axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40...... 202

Figure 4.17. WNV transmission fitted model of Cuyahoga County, Ohio, 2003, to actual mosquito infection rate (IR), using parameters in Table 4.5, with IR density represented by ▀. IR is not to scale with model estimated densities and used to illustrate peaks only.

Time (x-axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 ...... 203

Figure 4.18. Control strategy using larval 8% and adult 15% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 204

Figure 4.19. Control strategy using larval 10% and adult 20% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 205

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Figure 4.20. Control strategy using larval 10% and adult 30% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 206

Figure 4.21. Control strategy using larval 10% and adult 40% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 207

Figure 4.22. Control strategy using larval 10% and adult 0% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 208

Figure 4.23. Control strategy using larval 25% and adult 0% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 209

Figure 4.24. Control strategy using larval 50% and adult 0% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 210

Figure 4.25. Control strategy using larval 60% and adult 0% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 211

Figure 4.26. Control strategy using larval 0% and adult 15% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 212

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Figure 4.27. Control strategy using larval 0% and adult 20% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 213

Figure 4.28. Control strategy using larval 0% and adult 25% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 214

Figure 4.29. Control strategy using larval 0% and adult 30% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 215

Figure 4.30. Control strategy using larval 0% and adult 50% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 216

Figure 4.31. Parameter bite rate set at 5.5 bites per week per mosquito. Time (x-axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 219

Figure 4.32. Mosquito mortality rate decreased to 0.33 per mosquito per week. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002 ...... 220

Figure 4.33. Mosquito density (actual density divided by 100) and average infection rate by trap week, adulticiding by control week, and human cases by onset week for Franklin

County, 2002 ...... 224

xxviii

Figure 4.34. Estimated mosquito densities modeled with parameters in Table 4.5, except the gamma parameter was changed to 4.75 per capita/week and per capita per week (10 days instead of 12 days). Time (x-axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Franklin

County, Ohio, 2002 ...... 228

Figure 4.35. Control strategy using larval 12% and adult 5% weekly reduction. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Franklin County, Ohio, 2002. Estimated mosquito densities were modeled with parameters in Table 4.5, except the gamma parameter was changed to 4.75 per capita/week...... 229

Figure 5.1. Mosquito density (actual density divided by 100) and average infection rate by trap week, adulticiding by control week, and human cases by onset week for Franklin

County, 2003 ...... 241

xxix

Chapter 1: Introduction

1.1 Significance, rationale, and innovation

The West Nile virus (WNV) belongs to the Flaviviridae family of arboviruses

(arthropod-borne viruses), which are distributed worldwide. Dengue and Yellow Fever belong to the same family, as do St. Louis Encephalitis (SLE) and Japanese Encephalitis

(JE). Arboviruses are maintained in a complex enzootic cycle of least one non-human primary vertebrate host and a primary arthropod vector. Ecological conditions, particularly the relationship of the vector, host, and pathogen with temperature, precipitation and vegetative patterns can both mediate or unleash the enzootic cycles of each arbovirus. Epizootics among animals and epidemics among humans occur when this enzootic transmission cycle becomes unbalanced, usually triggered by ecological changes. Competent vertebrate hosts and primary or secondary vectors amplify the virus and transmit it to human or equine hosts (Gubler, 2001; Mostasharim, Kulldorff,

Hartman, Miller, and Kulasekera, 2003; Ruiz et al., 2010; Andreadis, Anderson,

Vossbrinck, and Main, 2004; Kilpatrick, Kramer, Jones, Marra, and Daszak, 2006; Ruiz,

Walker, Foster, Haramis, and Kitron, 2007). WNV disease in humans may cause systemic febrile illness, meningoencephalitis, and death. About 0.66% of people known to be infected develop severe illness. About 20% of people known to be infected develop

1 flu-like symptoms. About 80% of people known to be infected will be asymptomatic

(Centers for Disease Control and Prevention, 2013).

Most experts agree that there was a global emergence and re-emergence of arboviruses in the latter part of the 20th century, particularly WNV in the Western

Hemisphere and JE in the western Pacific at Saipan and the Torres Strait Australia

(Gubler, 2002). Many anthropogenic causes have been hypothesized for this re- emergence, including population growth, uncontrolled urbanization and land use changes over the past 50 years, changing agricultural practices, deforestation, insecticide resistance, increasingly virulent strains of WNV, poor housing, antiquated sewer and waste management systems, and modern transportation (Gubler, 1998; Gubler, 2001;

Patz, 2004; Artsob et al., 2009). There is also a body of literature linking emerging infectious diseases (EIDs) to climate change. Extreme weather events, such as drought in the spring and summer, have been associated with WNV epidemics (Epstein, 2004), including the 1999 United States (U.S.) epidemic.

The global spread of the WNV, its re-emergence in the Queens area of New York

City in the early spring or early summer of 1999, knowledge gaps recognized by prior statistical and mathematical research on the meteorological drivers of WNV, and the challenges faced while applying WNV mosquito control methods based upon this research, were major impetuses for this study. Significant questions about the U.S. and

Ohio’s WNV experience remain to be answered.

The medical and public health community first detected WNV in the Western

Hemisphere in New York City during the summer and fall of 1999. Once in the U.S., stakeholders were caught off guard due to 25+ years of complacency toward vector-borne

2 diseases (Gubler, 2002). This complacency intensified during the years after the successful campaign to control a widespread SLE epidemic in the Ohio-Mississippi River

Basin states in 1975. After controlling this epidemic, public policy and funding were not supporting continued mosquito control efforts due to the lack of perceived national public health threats (Gubler, 1998). By the 1980s, mosquito control efforts in Ohio had waned

(but in some counties and mosquito control districts, not disappeared), but were reawakened abruptly when WNV entered the northeast part of the state in 2001 from

Pennsylvania, giving state and local public health officials a two-year planning horizon from the time the epidemic began in New York City in 1999. Practitioners, researchers, and government agencies redesigned shelved mosquito-borne disease programs while policy makers adopted funding strategies. National, state and local mosquito control stakeholders, including the federal, state, and local public health communities, were forced to redevelop mid-20th century mosquito control methods into integrated pest management (IPM) programs safer to humans and the environment, that would include public education, surveillance, source reduction, larval control, and adult mosquito control, using chemicals sprayed from truck-based ultra-low volume (ULV) sprayers as the last public health protection strategy supported by the best available science (Marfin et al., 2001).

Local and state practitioners struggled with the development of control strategies to protect public health. For instance, the levels of mosquito density (i.e., mosquito abundance) or mosquito infection, sentinel infections, or epizootics that would trigger the activities of an IPM program, particularly truck-based spraying of adult mosquito control chemicals, were questioned by a discerning public worried about the chemical’s effect on

3 their own health and the health of the environment. By 2003, the Centers for Disease

Control and Prevention (CDC) had issued its guidance document proposing a four-tiered approach to control and prevention, but failed to offer the absolute minimum infection rate (MIR), or the number of positive mosquito pools per 1000 mosquitoes tested, which would trigger control strategies. CDC, 2003 cautioned: “Elevated infection rates, particularly if sustained over several weeks or in populations of opportunistic blood- feeders that may act as bridge vectors, are indicators of increased WNV transmission risk.” Hayes et al. (2005) estimated the risk of human WNV infection for Culex pipiens occurring at a MIR of 1/1000. In 2005, the Ohio Department of Health (ODH) issued a guidance document that set an action level for a public health response at 1/1000.

Ongoing research indicated that estimation methods using a maximum likelihood estimate (MLE), or an estimate of the number of infected mosquitoes per 1000 tested, would result in a more accurate estimate of transmission risk (Gu and Novak, 2004; Gu,

Lampman, and Novak, 2003; Chisenhall, Vitek, Richards, and Mores, 2008; Condotta,

Hunter, and Bidochka, 2004).

There is a large gap of current knowledge that this study intends to fill. Although many transmission dynamic studies claimed as an objective to “inform control strategies,” most of these studies were not translational in the purest sense because their study objectives and results did not include an evaluation of the application of the model by practitioners. One study’s model (Bouden, Moulin, and Gosselin, 2008) was calibrated in a simulated environment, and the authors concluded that results were promising and potentially beneficial if calibrated in real-life contexts (Cruz-Pacheco, Esteva, Montaño-

Hirose, and Vargas, 2005; Morin and Comrie, 2010; Bowman, Gumel, Van den

4 Driessche, Wu, and Zhu, 2005; Wohman, de-Camino-Beck, and Lewis, 2004; Hartley et al., 2012; Gong, DeGaetano, and Harrington, 2011).

It is entirely plausible that the decision-making algorithms guiding control strategies at the level of most practitioners have not changed significantly since the resurgence of WNV into the U.S.. An accurate prediction model will be developed to supplement or possibly replace the antiquated allegiance to, or reliance upon, the interpretation of biased minimum infection rate (MIR) or MLE of WNV infection in mosquitoes to drive control strategies, or the use of anecdotal evidence of weather patterns, mosquito density, or complaints from the bitten public, to predict mosquito

WNV infection patterns. This model will be translational and geared toward applied public health practice, which means that the model will have current science as its foundation, will be understandable to public health or mosquito control practitioners, practical and straightforward in its application, easily manipulated and run with free- source software, and valid in Ohio counties. To paraphrase the overarching goal of this translational research; to develop a model that practitioners will use in their mosquito control programs. Success will be measured by the model’s efficacy of performance in estimating WNV infection in humans and protecting public health.

In this study, the relationship between changes in mosquito biology and ecological conditions and WNV hazard in mosquitoes and risk to humans will be evaluated by analyzing the effect of indices informed by environmental predictors on mosquito infection rates (IRs). These indices were constructed as time-delayed time periods relative to the week mosquitoes were trapped (weeks before and during the trapping week). Specifically, these time periods estimated the temporal position of the

5 phases of the mosquito life cycle and the ecological conditions necessary for the development within these phases in relation to the trapping week. The first two indices, preoviposition and oviposition site development (POVPSD and OVPSD), are the estimated period of time needed to create the optimum oviposition site resulting from the ecological conditions necessary for oviposition. The third through sixth indices were oviposition (OVP), eggs-to-hatch (E_H), the gonotrophic cycle (GON, which includes the EIP), and an overwintering period from December to March. These indices will assist practitioners to make decisions on control measures based upon the significance of the unique phases of the mosquito life cycle and its ecology. This research will contribute to the improvement of scientific knowledge by informing the indices with a novel WNV hazard independent predictor, the 7-day Palmer Drought Index (PDI), along with other meteorological predictors (such as temperature, precipitation, and day length or photoperiod), which may help to advance the science of estimating human WNV disease risk. No previous statistical studies of WNV hazards using this approach were found.

This study will improve upon previous studies by informing, developing, and calibrating a mathematical model (MM) using results from the statistical analysis of the association of 2002 empirical meteorological data from Ohio with WNV infected mosquitoes. This

MM will be applied to individual counties using 2002 and 2003 data, to gain insight to the drivers that may have influenced IRs, and will be further tested for its predictive capabilities on an independent 2012 IR dataset and 2012 meteorological data.

The response variables, MIR or MLE, were calculated with the ODH’s historical mosquito surveillance dataset from gravid traps (with the exception of Lake County), which collect female mosquitoes during the OVP period of the GON. Theoretically, this

6 method will improve upon previous studies, which have relied on surveillance data from

CDC light traps, which capture female biting mosquitoes that are being attracted to a simulated human source of CO2 (Morin and Comrie, 2010; Bouden et al., 2008; Gong et al., 2011; Andreadis et al., 2004; Liu et al., 2009; DeGroote, Sugumaran, Brend, Tucker, and Bartholomay, 2008). The use of CDC light trap surveillance data did have limitations because it resulted in models overestimating mosquito densities. These models used equations constructed with parameters that were initialized with literature value estimates of egg and subsequent mosquito densities, but were compared (during calibration) to actual adult mosquito capture counts from CDC light traps (Gong et al.).

1.2 Study framework and overall research objective

Figure 1.1 presents a flow chart of the study framework. An overall objective was written to help guide the development of study questions, hypotheses, specific aims and methods. From this objective, specific questions were developed and supported by the hypotheses that will be tested to answer the questions. A specific aim was written that described the research activity necessary to test each hypothesis. To support the specific aims, research methods specifically outline the methodologies that were used to test each hypothesis, and rationales are presented to support these methods. Descriptive analyses will assist in characterizing Ohio WNV infection in mosquitoes and humans. A statistical analysis was completed to better understand the drivers of WNV infections. The results of the statistical analysis inform a conceptual MM, which was calibrated using Ohio

WNV mosquito infection data. The calibrated model was evaluated on its predictive strength using an independent dataset, and then connected to real-life disease management. The overall research objective was to understand the biological and

7 meteorological drivers associated with WNV infection in mosquitoes and to develop a predictive model to inform public health surveillance and intervention practices. This study explored four research questions to meet this objective:

Study Framework

Overarching Research Goals and Objectives Application of Predictive Mathematical Model in Specific Research Real Life Context Questions

Mathematical Model Hypotheses Calibration on Independent Dataset

Specific Aims Mathematical Model Calibration on Historical Dataset Methods

Conceptual Statistical Empirical Mathematical Model Descriptive Analyses Analyses Formulation

Figure 1.1. Flow chart of the study framework including overarching goals and objectives, research questions, hypotheses, specific aims, methods, descriptive, analytical and mathematical studies

1.3 Research questions

1.3.1 Research question 1

What are the temporal patterns of weekly mean temperature (T), weekly cumulative precipitation (CP), the PDI, and weekly mean day length (DL) that are related to documented WNV mosquito infection? Is there a spatial progression of reported 8 human WNV disease and mosquito IRs from northern to southern Ohio? What are the temporal patterns of reported human WNV disease, WNV positive bird deaths, mosquito

IRs, mosquito density (abundance), and mosquito control applications?

1.3.2 Research question 2

What are the biological and meteorological determinants of WNV infection in mosquitoes and humans?

1.3.3 Research question 3

This study attempted to answer these specific questions: 1) Can a bird/mosquito

WNV transmission model be developed and calibrated that could predict both sub-apex and apex peaks of WNV transmission in infected mosquitoes from the Ohio, 2002, dataset of IRs? 2) Can the model developed with the Ohio, 2002, IR dataset be fitted to individual counties during 2002 and 2003? 3) Can the model developed provide insights to the influence of mosquito density and mosquito infection on the transmission of WNV hazards? Which one is more important to the practitioner when making decisions to control WNV transmission hazards? 4) Can the model developed give insights on what key control practices or combination of practices that minimize cost and maximize the benefit of hazard reduction? What component of the transmission cycle is control most cost-effective to reduce transmission hazards?

1.4 Research hypotheses

1.4.1 Research hypothesis 1

The primary research hypotheses explored were that increases in 2002 mosquito

IRs from the beginning of the mosquito season to week 34 will be associated with the following patterns: 1) increasing T during the gonotrophic (GON), eggs-to-hatch (E_H),

9 oviposition site development (OVPSD) time-delayed indices, and the oviposition (OVP) index; and 2) decreasing CP during the E_H time-delayed index; 3) a decreased CP, increased T, and decreased PDI during the 2002 overwintering time-delayed index compared to 2003-2006; 4) a decreasing PDI during the preoviposition site development

(POVPSD) and OVPSD time-delayed indices; 5) an increased amount of CP during the

POVPSD time-delayed index compared to a decreased amount during the OVPSD time- delayed index; and, 6) decreasing DL during the OVP index and the GON time-delayed index. Three additional hypotheses were explored: 1) decreasing 2002 mosquito density

(abundance) after week 34 will be associated with a decrease in DL; 2) increasing 2002 mosquito IRs will be associated with an increase in WNV infection in humans within two to three weeks after the week of trapping, and mosquito IR increases will be preceded by increases in mosquito abundance and bird deaths from WNV; and, 3) these data will also demonstrate a temporal and spatial progression of reported mosquito IRs and human

WNV disease onsets, beginning first in southern metropolitan areas and temporally and spatially moving to northern metropolitan areas of Ohio.

1.4.2 Research hypothesis 2

The hypotheses explored in this research were that significant increases in IRs will be associated with: 1) an increase in T during the OVP index, and the GON, E_H,

OVPSD, and overwintering time-delayed indices; 2) a decrease in CP during E_H,

OVPSD, and the overwintering time-delayed indices; 3) an increase in CP during the

POVPSD time-delayed index; 4) a decrease in the PDI during overwintering, POVPSD and OVPSD time-delayed indices; and, 5) an increase in mosquito WNV IR will be

10 associated with a significant increase in WNV infection in humans within two weeks after the week of trapping.

1.4.3 Research hypothesis 3

The following research hypotheses were explored: 1) A model will be parameterized and calibrated with literature values and significant temperature and precipitation indices of WNV infection in mosquitoes resulting from the answers to research question 2, which will predict peak Ohio, 2002, actual mosquito IR; 2) The model developed will be recalibrated by changing parameter values and fit to 2002 and

2003 actual mosquito IR for Franklin and Cuyahoga Counties; 3) Mosquito density and mosquito IR will be equally important in the control of WNV transmission hazards; and,

4) Control practices will be most effective if applied to the larval model component, and, when combined with a 60% reduction in the adult mosquito population, results in the most cost-effective control method.

1.5 Specific aims

1.5.1 Specific aim 1

Time-delayed indices will be constructed as time periods relative to the week mosquitoes were trapped (weeks before and during the trapping week). These indices will estimate the temporal position of phases of the mosquito life cycle, and the ecological conditions necessary for the development within these phases, in relation to the trapping week and will be informed with T, CP, DL, and the PDI. Descriptive statistical tools will be used to characterize temporal and spatial patterns of: 1) T, CP, DL, and the PDI related to documented WNV mosquito infection; and, 2) reported human WNV disease,

11 WNV positive bird deaths, mosquito IRs, and mosquito density (abundance), by week, year and Ohio County, and within the broader context of the U.S..

1.5.2 Specific aim 2

Observational data from the ODH 2002-2006 historical dataset of mosquito IRs and human WNV case onset dates will be used to determine the biological and meteorological drivers underlying WNV infections in mosquitoes and humans, using regression modeling methods of an extensive empirical dataset that integrates information learned from specific aim 1. The initial set of significant indices to be added into the model will result from a univariate analysis. Indices which significantly explain the variation of mosquito WNV infection will be included in the final model, with consideration given to indices that were included based upon practical experience with the Franklin County Public Health IPM program.

1.5.3 Specific aim 3

A MM will be developed by integrating functions containing meteorological drivers of WNV disease transmission and literature parameter values into differential equations in order to gain insight into the biological processes fundamental to increased

WNV infection in mosquitoes. The MM will be developed, evaluated, and calibrated at the state and county levels with the same dataset that resulted in the identification of statistically significant drivers in specific aim 2. The evaluation of the model will determine if there is a favorable comparison to the outcomes of WNV infections in mosquitoes. If there is not a match between the predicted and observed outcomes, the parameters, functions, or differential equations will be adjusted accordingly (Oreskes,

Shrader-Frechette, and Belitz, 1994). The model will be evaluated and re-calibrated using

12 IR data from the same counties and a different year of the study, or a county from a year outside of the study time frame with high mosquito IRs. The evaluation of the model will determine if there is a favorable comparison to the outcomes of WNV infections in mosquitoes in the independent dataset. If there is not a match between the predicted and observed outcomes, the parameters, functions, or differential equations will be adjusted accordingly (Oreskes et al., 1994). The model will then be connected to real-life disease management strategies by modeling the effect of interventions on WNV transmission.

1.6 Organization of the dissertation

The organization of this dissertation follows the progression of research outlined in the specific aims. Chapter 1 is an introduction to a broad range of fundamental topics that are relevant to this research, and to the practice of controlling human WNV infection risk from mosquitoes. Chapter 1 provides a research flow chart for the entire project, establishes a rationale and justification for its importance, describes its significance to the science of vector control and hazard reduction, and provides qualification of its innovation.

Chapter 2, Chapter 3, and Chapter 4 are written as three manuscripts reporting on specific aims 1-3, respectively. Chapter 2 is a descriptive study of 2002-2006 data for

WNV in mosquitoes, birds, and humans, as well as meteorological data such as temperature and rainfall. This preliminary study characterizes and describes trends that may be significant associations between WNV in mosquitoes, bird mortality, and mosquito ecology, and between WNV in mosquitoes and humans. Chapter 3 takes what was learned from Chapter 2 and applies statistical methods to estimate whether significant associations exist between the same predictor and outcome measures used in

13 Chapter 2. Chapter 4 takes what was learned from the results of the statistical methods used in Chapter 3 to develop, parameterize, calibrate, and validate a MM, that takes into account the dynamic nature of the WNV transmission cycle, for the purpose of predicting and controlling WNV hazards. Chapter 5 synthesizes and summarizes the results from the previous three Chapters, with emphasis on the effect on public health by the application of the models designed by this study to mosquito management, control, and hazard reduction.

1.7 Background

1.7.1 The West Nile ribonucleic acid (RNA) virus

The WNV is an arthropod-borne RNA virus belonging to the family of

Flaviviridae and genus Flavivirus, and with SLE, JE, Murray Valley Encephalitis in

Australia, and the Kunjin virus (KUN) of Australia and Asia belongs to the same serogroup as the JE virus (Brinton, 2009; Gubler, 2007; Scherret et al., 2001).

Morphologically, the WNV is a spherical, 50 nm size virus, with a lipid layer (host- derived from the endoplasmic reticulum) surrounding a protein core. The protein core houses a single-stranded positive-sense RNA genome composed of approximately 11,000 nucleotides, which functions like messenger RNA (mRNA), and have the capability to translate (by vector and host cell) ribosomes directly into the amino acids and proteins needed for transcription/replication (Brinton, 2009; Scherret et al., 2001).

The spherical virus shown in Figure 1.2 is constructed with Envelope (E), premembrane/membrane (prM/M), and capsid (C) proteins. Immature virions contain an

E-prM complex of 60 trimers of prM-E heterodimers taking the form of 60 spikes on the virion surface. Mature virions have 90 parallel E dimers organized in rafts of three, lying

14 flat against the lipid layers. These rafts are arranged in a herringbone-like anti-parallel design on the virion (Brinton, 2009; Heinz and Stiasny, 2012). The E protein’s function is to facilitate host cell membrane binding and cell entry at recognized receptor sites, is involved in flavivirus assembly, and has antigenic properties recognized by antibodies.

Figure 1.2. Depiction of West Nile spherical virus constructed with Envelope (E), premembrane/membrane (prM/M), and capsid (C) proteins (ViralZone, SIB Swiss

Institute of Bioinformatics, 2013, with permission)

The antibody-mediated immune system of hosts is essential to control the dissemination and replication of the virus. Because of these antigenic properties, it has been the target of research to develop vaccines to induce antibodies to neutralize receptor binding, and to inhibit the fusion process (Pierson, Fremont, Kuhn, and Diamond, 2008;

Wang, He, and Anderson, 1999). To promote transport within the cell, one of the three E protein domains is hydrophilic. The C protein composes the ~30nm diameter capsid and surrounds the RNA genome, and also contains a hydrophobic domain, which functions in 15 viral assembly in association of the rough endoplasmic reticulum (Pierson et al.; Schlick et al., 2009). The virion genome has non-coding regions at the 5´ end (96 nucleotides) of the genome and the 3´ end of the coding region (337- 649 nucleotides), and coding regions for C, M, and E proteins (Figure 1.3). Non-structural coding areas of 600 nucleotides are labeled NS1, NS2A, NSB, NS3, NS4A, NS4B, and NS5 (Brinton, 2009;

Campbell, Marfin, Lanciotti, Gubler, and Nile, 2002; De Filette, Ulbert, Diamond, and

Sanders, 2012).

Figure 1.3. West Nile virion genome showing structural and non-structural proteins (De

Filette et al., 2012)

To replicate, a mature WNV must first bind with the cell’s plasma membrane receptors recognized by protein E. Through receptor-mediated endocytosis, (Figure 1.4) the virion enters the cellular endosome and its acidic (PH < 6.6) environment, where the

E protein is irreversibly changed to trimers, which facilitates fusion of the virion membrane with the endosomal membrane and the release of the capsid and RNA genome into the cellular cytoplasm (Brinton, 2009; Heinz and Stiasny, 2012). The positive sense

RNA genome is then translated in the cytoplasm using the host cell’s ribosomes into a

16 poly-protein, which is then cleaved to produce the three mature structural proteins, and seven mature non-structural proteins, including NS5, the RNA-dependent RNA polymerase (RdRp) within the lumen of the ER by hosts’ enzymes or the enzyme encoded from NS3.

Figure 1.4. West Nile virion entering the cellular endosome, releasing the capsid and

60 trimers of prM-E heterodimers to 90 parallel E dimers, organizing in rafts of three and lying flat against the lipid layers, and completing the replicating cycle

17 Associated with rough endoplasmic reticulum (ER) membrane, and with the aid of RdRp, the cleaved poly-proteins replicate into negative sense copies aided by RdRp, which serve as copies for many positive sense infectious RNA genomes. The pathway from positive sense RNA genomes to the assembly of mature virions begins at the membrane of the ER. The new viral membrane (envelope) is derived from the membrane of the ER.

Immature virions moving to the lumen of the ER consist of the 60 trimers of prM-E heterodimers spikes. The virions move through the cell’s secretory pathway to the acidic compartments of the Golgi bodies, where enzymes cleave the prM protein. The acidic environment causes E proteins to undergo a structural transition of 60 trimers of prM-E heterodimers to 90 parallel E dimers, organized in rafts of three and lying flat against the lipid layers. Thus, the replication cycle is complete (Brinton, 2009; De Filette et al.,

2012; Heinz and Stiasny, 2012).

The virion has been separated genetically into two lineages, I and II. Lineage I associates with human West Nile virus encephalitis and lineage II does not cause human illness, but is maintained in an enzootic cycle in Africa. The New York, 1999, epidemic has been identified as a lineage I virus. The closest genetic relative of the New York,

1999, epidemic was isolated from a dead goose during a 1998 epizootic in Israel having

99.8% of a 1278 nucleotide sequence belonging to lineage I (Gubler, 2007; Hayes, 2001;

Petersen and Roehrig, 2001). Kilpatrick, Meola, Moudy, and Kramer (2008) studied two genetically different strains of WNV, WN99 and WN02. WN99, the virus introduced into the U.S. in 1999, which has evolved since its introduction into a more efficient strain with changes in its genome, and it is now labeled WN02-1956, (Genbank accession number

AY590210).

18 1.7.2 Epidemiology of human WNV disease, 1937-2012

1.7.2.1 An emerging infectious disease: WNV from the Eastern to the Western

Hemisphere

In the Eastern Hemisphere, WNV is not a new disease. The first known human isolate of the WNV was from the blood of a north-western Ugandan female in 1937, who lived in the West Nile District (Smithburn, Hughes, Burke, and Paul, 1940). The region received its name from being located on the western side of the White Nile River, a major tributary of the Nile River. In 1950, isolates were identified in Egypt, and epidemiologic studies from 1952 and 1954 found endemic areas in the southern part of the Nile delta, in densely populated areas where land was under cultivation using irrigation, compared to the northern delta regions where land was not being cultivated. The first epidemics in

Egypt were identified in the same time period, south of Tel Aviv, in a wet region along the Mediterranean coastal plain. Researchers were able to show that the WNV isolated in

Uganda was related to SLE and JE. In the 1960s, WNV was isolated in the Rhone Delta region of France and the Volga Delta region of Russia. By 1965, the virus spread to the

Middle East, Europe, and Asia, prompting researchers to divide the isolates into three antigenic groups: an African-Middle-Eastern, Indian, and South African. In 1974, an epidemic occurred in South Africa after extreme precipitation events inundated a region that usually remained dry.

By 1990, with newer nucleic acid sequencing techniques, two lineages emerged (I and II). Three large urban epidemics occurred in the mid to late 1990s, in Southern

Romania (1996), in the Volgograd region of Russia (1999), and in the northeastern U.S. in 1999. These three areas shared similar ecological characteristics of densely populated

19 urban areas surrounded by suburbs and rural areas, and next to large rivers believed to have floodplain areas or wetlands. Researchers identified wild or domestic birds as hosts and Culex pipiens as the major vector. There were lower than normal amounts of rainfall during the summers of the epidemic, producing stagnant polluted breeding sites (Hayes,

2001).

It may never be known how the WNV was introduced into the northeastern U.S..

Its distribution westward across the country was a swift and devastating epizootic/epidemic. Experts suggest that a new, highly virulent strain of the Lineage I virus emerged from 1994-1996, just before the Romanian epidemic; the same strain that produced the Israeli geese epizootic and entered New York City in 1999 (Artsob et al.,

2009). A virus with this degree of pathogenicity had not been seen since 1962, during an epizootic among horses in France exhibiting a case fatality rate (CFR) of 25-30%. The

Israel (2000), Romania (1996), Russia (1999) and the New York (1999) epizootic/ epidemic were all associated with this highly virulent, severe, and fatal neurological disease in equines and humans (Gubler, 2007). As described by Gubler (2007), “a virus with greater virulence and epidemic potential emerged that most likely had better fitness, and that yielded higher virus loads in susceptible hosts, allowing it to take advantage of modern transportation and globalization to spread in the Mediterranean region, to Europe, and then to the Western Hemisphere.” Unknown to the public health community in the

U.S., there had been outbreaks of neurological disease in domestic geese in Israel from

1997-2000. It is suggested that the origin of the New York City WNV was the

Mediterranean region. An isolate from a dead goose in Israel from the 1998 epizootic was traced back to Lineage I from North, West, and Central Africa; Southern and Eastern

20 Europe; India; and the Middle East (Gubler, 2002; Gubler, 2007; Hayes, 2001; Scherret et al., 2001). In 1999, following the geese epizootic, there was a human outbreak in Tel

Aviv Israel where only two patients died, but these cases were not recognized by the world health community as an “epidemic.” In 2000, 49 years after WNV was first reported in Israel in 1951, and after severe drought conditions in that country, an epidemic of 417 human cases were serologically confirmed with 35 deaths (Epstein,

2004; Gubler, 2002).

In August, 1999, a group of elderly patients in New York City were observed with viral encephalitis, and originally clinically diagnosed with SLE, but subsequent laboratory results confirmed WNV (Artsob et al., 2009; Gubler, 2007). WNV disease in humans causes systemic febrile illness, meningoencephalitis, and death. About 0.66% of people infected develop severe illness. About 20% of people infected develop flu-like symptoms. About 80% of people infected will be asymptomatic (CDC, 2013). By the end of the year the epizootic/epidemic spread to four states (New York, New Jersey,

Connecticut, and Maryland) and 62 cases of severe neurological disease in humans were reported with seven deaths, a 25% CFR for horses and thousands of bird deaths.

In 2000, there were 21 human cases and two deaths, and dead birds were detected with WNV along the Atlantic coast to North Carolina (Gubler, 2002). In 2001, a WNV epizootic/epidemic moved into the east central states as far as Iowa and Louisiana and into Ontario, Canada and an epizootic moved south to Mexico, Central America and the

Caribbean (Arstob et al., 2009). There were 66 human cases and ten deaths (15% CFR) in the U.S. in 2001. The WNV epizootic moved into Ohio in 2001 and the epizootic/epidemic peaked in 2002 with 310 neuroinvasive cases and 31 deaths (10%

21 CFR). In 2002, the epicenters of transmission were from Louisiana to the north central states of North Dakota and South Dakota, resulting in 4156 cases reported, with 2946 neuroinvasive diseases with a 10% CFR in the U.S.. By 2003, the virus reached

California and a staggering 9862 total human cases, and 2866 cases of neuroinvasive diseases were reported with a CFR of ~10% (264) nationwide. The 2003 epicenter was farther west in the Midwest and central plains states. It should be noted that the distribution of the epizootic preceded human cases (Artsob et al., 2009; Gubler, 2007).

By 2003, epizootic activity was recorded in mainland South America and human cases were reported in Cuba. In 2003, the first human case was documented in Cuba. In

2004, a WNV epizootic was active in Columbia, and the first human case was recorded in Haiti, followed by a human case in Columbia in 2005, an epizootic in Argentina in

2005 and human cases in Argentina in 2006 and 2007 (Arstob et al., 2009). Table 1.1 displays the U.S. human neuroinvasive case totals from 1999-2012.

There was a steady decline of human WNV cases nationwide from 2004-2011. A resurgence of epicenters of transmission formed from the upper to lower Midwest in

2012. It is not yet known the combination of ecological factors that contributed to this resurgence, but dryer and warmer winters have been speculated, as well as host (bird) competence and density, vector density, and human behavior. The disease cases reported in 2012 were the highest since the 2003 epidemic. CDC (2013a) reported that the national incidence for neuroinvasive disease increased to 0.92 per 100,000 in 2012, compared to

1.02 in 2002, 0.98 in 2003, and a median on 0.32 from 2004-2011. Residents of North

Dakota, South Dakota, Texas, Louisiana, and Mississippi, and persons of age ≥ 70 had the highest incidence.

22 Total CFR Year Neuroinvasive Deaths Cases (%) 1999 62 59 7 12 2000 21 19 2 11

2001 66 64 10 15 2002 4156 2946 276 10

2003 9862 2866 264 10 2004 2539 1148 94 8

2005 3000 1309 104 8 2006 4269 1495 162 11

2007 3630 1227 117 10 2008 1356 689 41 6

2009 720 386 32 8 2010 1021 629 54 9 2011 712 486 42 9 2012 5674 2873 270 9

Table 1.1. Total U.S. human WNV cases, neuroinvasive cases, deaths, and case fatality rates per year, 1999-2012 (CDC, 2013)

Figures 1.5 A is the epidemic curve for WNV cases in the U.S. from 1999-2008, which is the cumulative data closest to the years studied in this research, demonstrating that epidemiologic week 34 was the peak onset of cumulative disease incidence, with additional smaller peaks at week 37 and 38. Figures 1.5 B and 1.5 C illustrates the annual incidence of WNV disease by year and the cumulative incidence of WNV disease for

1999 - 2008 by county, respectively. The 14 year cumulative incidence map clearly shows that concentrations of WNV disease incidence were spread from the upper to lower Midwest and in the Mississippi River delta states of Texas, Louisiana, and

Mississippi. Figure 1.5 D indicates the age group of ≥ 70 years was the highest in annual average incidence for West Nile virus neuroinvasive disease for years 1999-2012.

23 A B

C D

Figure 1.5. West Nile Virus United States cumulative epidemic curve from 1999-2008

(A), annual disease incidence by year, 1999-2008 (B), cumulative incidence by county,

1999-2008 (C), and average incidence of neuroinvasive disease by age from 1999-2012

(D)

The CDC, through its Morbidity and Mortality Weekly Report (MMWR), releases timely descriptive epidemiological studies of WNV. There have been studies of WNV disease in 1999-2008, 2009, 2010, 2011, and 2012. In a study on WNV disease from

1999-2008 in the U.S., CDC (2010) reported epidemiological surveillance data. This study was chosen for review because of the epidemic proportions of human WNV disease

24 in the U.S. from 2000-2003 and again in 2006. The following limitations of the study were noted: 1) data collected from ArboNet, a U.S. cooperative mosquito surveillance system, is a passive surveillance system, and the incidence of disease is probably underreported; 2) race and ethnicity data were not imputed; 3) misclassification of the clinical symptoms; and, 4) disease incidence may not be representative of surveillance data collected with higher resolution. Fifty-eight percent of the neuroinvasive disease cases were male, 88% were white and 82% of the patients who reported ethnicity were non-

Hispanic (demographic data are presented in Appendix A). “Among neuroinvasive disease cases, 7,502 (63%) were reported as encephalitis, 3,930 (33%) as meningitis, and

311 (3%) as acute flaccid paralysis (AFP); in 79 (< 1%) cases, the type of neuroinvasive disease syndrome was unspecified” (demographic characteristics by clinical syndrome are found in Appendix B), and case definitions and clinical presentations in Appendix C

(CDC, 2010). The median age of the cases was 57, and almost half of the cases (46%) occurred in patients age ≥ 60 years, the hospitalization rate for persons age ≥ 50 years was 92% compared to persons age < 50 years old (85%), and 90% of the patients had an onset during July to September (CDC, 2010). The overall case fatality rate was 9%, which increased with age, with the combined case fatality rate at 17% for patients aged

60-69 and ≥ 70 years. An extremely small percentage of patients (0.01%) may have contracted the disease from non-mosquito sources such as blood transfusion or an organ transplant (CDC, 2010).

“The national average annual incidence of neuroinvasive disease during 1999-2008 was 0.40 per 100,000 population (range: 0.01-1.02). Despite substantial geographic spread of the virus during 1999--2001, neuroinvasive disease incidence remained low until 2002 (1.02) and 2003

25 (0.99), when outbreaks centered in the Midwest and Great Plains occurred. The national incidence of neuroinvasive disease was stable during 2004- 2007 (mean: 0.44; range: 0.39-0.50). In 2008, the incidence was 0.23 per 100,000 population, compared with 0.41 in 2007 and 0.50 in 2006. Similar trends over time were observed for each clinical syndrome (encephalitis, meningitis, and AFP)” CDC (2010).

“Neuroinvasive disease cases were reported from 46 states and DC; 63% of all cases were reported from 10 states: Arizona, California, Colorado, Illinois, Louisiana, Michigan, Mississippi, Nebraska, South Dakota, and Texas. No neuroinvasive disease cases were reported from Alaska, Hawaii, Maine, or Vermont. The average annual incidence for all states ranged from <0.1 per 100,000 population (New Hampshire, North Carolina, South Carolina, Washington, and West Virginia) to 4.0 per 100,000 (South Dakota). States with the highest average annual incidence were in the West North Central and Mountain states. Colorado, Idaho, Louisiana, Mississippi, Montana, Nebraska, North Dakota, South Dakota, and Wyoming had an average annual incidence of ≥1 case per 100,000 population. Counties with the highest average annual incidences also were clustered in West North Central and Mountain states” CDC (2010).

“The average annual incidence of neuroinvasive disease increased steadily with increasing age, ranging from 0.05 per 100,000 among persons aged <10 years to 1.35 among those aged ≥70 years. Similarly, the average annual incidence of encephalitis increased with increasing age, ranging from 0.02 per 100,000 among persons aged <10 years to 1.10 among those aged ≥70 years. The average annual incidences of meningitis and AFP increased with increasing age among persons aged <40 years but did not change among persons aged ≥40 years” CDC (2010).

“Neuroinvasive disease incidence was higher among males (0.48 per 100,000 population) than among females (0.33), especially among persons aged ≥60 years, for whom the incidence in men was twice that in women. Similar differences by sex were observed among the various neuroinvasive disease syndromes (i.e., encephalitis, meningitis, and AFP)” CDC (2010).

Underreporting was an additional concern which may have biased the magnitude of the epidemic (as its epicenters made their way from the East to the Midwest, to the

West, and back again to the Midwest). It is estimated that 20% of the neuroinvasive cases

26 were underreported to the U.S. ArboNET from states and local governments (Busch et al., 2006).

The economic cost of medical treatment and public health response has been calculated in only two jurisdictions in the U.S.: Louisiana, and Sacramento County,

California. “The estimated cost of the Louisiana epidemic was $20.1 million from June

2002 to February 2003, including a $10.9 million cost of illness ($4.4 million medical and $6.5 million nonmedical costs) and a $9.2 million cost of public health response”

(Zohrabian, et al., 2004). In 2005, the total cost to Sacramento County for medical treatment and patient productivity loss was $2.98 million (Barber et al., 2010).

Zohrabian, Hayes, and Petersen, (2006) reported that the societal cost of death from

WNV disease using productivity loss tables was $200,000, and the cost of lifelong disability was estimated at a baseline of $210,000. The cost per case of neuroinvasive

WNV disease with full recovery was estimated at $27,500 (Zohrabian et al., 2006).

Assuming 1.4% of a hypothetical population of 100 million people over a ten year period were infected, and the average cost of a case prevented was $34,500, “at a cost of $8.7 billion in a hypothetical population of 100 million people, vaccination would prevent

256,000 cases of WNV illness, including 9,216 cases of neuroinvasive disease, 2,935 cases of lifetime disability, and 829 deaths during a ten-year period” (Zohrabian et al.,

2006). Under these assumptions, the vaccine cost would need to be < $12.80 or the lifetime disability costs > $3.2 million (Zohrabian et al., 2006).

27 1.7.3 WNV transmission dynamics

1.7.3.1 The effect of temperature on mosquito biology and the transmission of

infection.

Experimental evidence suggests that just because a mosquito is infected with

WNV, does not automatically mean it is infectious, i.e., it can infect a host (Hartley et al.,

2012). The effect of climate on the estimated length of the mosquito EIP and the GC were important to this research. Scientists have found that changes in ambient temperature and mosquito species play an important role in virus transmission dynamics, such as: 1) the EIP for horizontal transmission (mosquito to bird, or dead end hosts such as humans), the time period from when the mosquito becomes infected to becoming infectious, and including time for the virus to amplify and disseminate through the body of the mosquito from the gut to the legs and then to the salivary glands (Anderson, Main,

Delroux, and Fikrig, 2008); and, 2) the GC, the minimum time between two consecutive blood meals that are necessary for egg development prior to oviposition (Wonhan and

Lewis, 2008). The GC includes the time it takes to locate a blood meal, for egg development, and the time needed for oviposition (Lardeux, Tejerina, Quispe, and

Chavez, 2008).

Vinogradova (2000) stated that the GC of Culex pipiens pipiens, an urban vector of WNV, was a four to eight-day cycle at 25°C and longer at lower temperatures. Gad,

Feinsod, Soliman, and El Said (1989) assigned three days as the GC length in their research estimating survival of Culex pipiens. Tachiiri, Klinkenberg, Mak, and Kazmi

(2006) used an average of five GCs per season for Culex pipiens in their human WNV risk modeling efforts. Few researchers have exclusively studied the U.S. species Culex

28 pipiens pipiens, the northern house mosquito. Anderson et al. (2008) used reverse transcription polymerase chain reaction (RT-PCR) to measure the transmission of infection from Culex pipiens pipiens which fed on a membrane feeder containing blood with 2.7 x 106 plaque forming units (PFU)/ml WNV to suckling mice at 26oC, and found that transmission increased with longer EIPs…11-28% during 8-13 days post infection

(dpi), 47%-54% during 14-15 dpi, and 75%-100% during 16-25 dpi. The percentage of transmission also increased with an increase in blood meals at a constant 26oC, with 73% and 100% of mosquitoes transmitting between 12 and 23 dpi, respectively, suggesting a positive association between transmission, the number of blood meals (gonotrophic cycles) and the length of EIPs at a constant temperature. Dohm, O’Guinn, and Turell

(2002) reported 4-12 dpi at 30°C and 28 dpi at 18°C for Culex pipiens pipiens that were exposed to chickens inoculated with 103 PFU/ml WNV. In this study, unlike Anderson et al. (2008), where blood was tested for WNV from the host (sucking mice) using RT-

PCR, transmission was identified by the virus being disseminated to both the body and the legs of the mosquito using plaque assays on Vero cell monolayers. In another laboratory experiment of Culex tarsalis, a rural Western U.S. vector of WNV, degree days (DD) or °D were calculated to quantify the accumulated degrees above a certain threshold, by multiplying the number of days needed for mosquitoes to transmit the

WNV after feeding off of an infectious host by the difference between the threshold temperature at which no viral replication occurs and the experimental temperature. For example, if seven days were needed for transmission at 30°C, and the threshold was 14

°C, then the DD for transmission would be calculated at 112 DD. The threshold value for

Culex pipiens pipiens has been estimated to lie between 10°C and 14°C (Reisen, Fang,

29 and Martinez, 2006). Reisen et al. (2006) calculated the EIP50 DD for Culex tarsalis to be

109 DD with the determination of transmission by RT-PCR if both the body and legs of the mosquito tested positive for the virus. Kilpatrick, Meola, Moudy, and Kramer (2008) noted that a simple linear DD model would not accurately describe the time needed for viral transmission due to exponential temperature-driven viral amplification processes.

Instead, the DD model used in their experiment included the term that was the product of days since feeding and temperature, raised to the fourth power. Culex pipiens mosquitoes were fed goose blood with a NY99 or a WN02 titer of 1.2-1.4 x 108 PFU and then held at either15°C, 18°C, 22°C, or 32°C until being sampled at various intervals of time.

Infection, dissemination, and transmission were measured by Vero cell plaque assay if the virus was detected in the gut, the legs, or in salivary secretions, respectively. The

WN02 strain transmitted at 12, 36, and 72 hours at 32°C, and the WN99 strain transmitted on day three. At 32°C, approximately twice the percentage (0.8 versus 0.4) of

WN02 transmitted compared to WN99 at 450 DD (Kilpatrick et al., 2008). Results from this study led to the conclusion that small increases in temperature can have exponential effects on transmission rates, most likely caused by an increase in virion replication rates resulting from shorter EIPs. When temperatures are less than 32oC, the replication cycle of the WNV virion is between 10-12 hours, and transmission rates decrease (Kilpatrick et al.).

Nielson et al. (2008) analyzed IRs of Culex pipiens complex and Culex tarsalis and temperature to discover associations with human WNV disease. Using a DD model,

Nielson et al. calculated an estimated number of EIPs by dividing the cumulative total

DD by 109°C previously determined by Reisen et al. (2006) and found that the beginning

30 of the second and third EIPs associated with the peaks in human WNV cases. O’Connor,

Gingrich, Unnasch, and Hassan (2009) identified parous/gravid (defined as having laid at least one batch of eggs and are seeking second or third blood meal)/egg-bearing) Culex pipiens, collected from June 15 to October 20 by ovary dissection, and found that the percent of parous/gravid increased from 40%-80% from the beginning to the end of the collection season, suggesting that gonotrophic age is older at the end of the season.

O’Connor et al. (2009), as part of their research, reiterated the results of previous studies that had reliably measured the GC at four days with average temperatures.

Hartley et al. (2012) characterized the relationship between temperature and GCs and EIPs as temperature dependent. When temperatures rise, the GC of the mosquito and the EIP of the virus shortens and a shortened GC increases the biting rate and the oviposition rate, which increases mosquito density. When the EIP is shortened, mosquitoes can transmit the virus at a younger age. In warmer temperatures Culex tarsalis becomes more efficient at transmission and can complete an EIP within two GCs

(Focks, Brenner, Hayes, and Daniels, 2000; Hartley et al., 2012). The “probability of an infected female mosquito surviving long enough to become infectious (transmit the virus) increases significantly at higher temperatures because EIPs are shorter” (Focks et al.,

2000). Vector competence was evaluated on Culex restuans and salinarius by Sardelis,

Turell, Dohm, and O’Guinn, (2001), who fed mosquitoes on two day-old chickens inoculated with 103 PFU WNV, incubated the mosquitoes at 26°C for 14 days (i.e., a 14 day EIP), and identified them as infectious if WNV was found in both the body and legs.

Sardelis et al. (2001) found that when donor chickens blood contained a viral titer of

≥106.3 PFU WNV, ≥ 84% of Culex spp. disseminated.

31 Four additional periods of the mosquito life cycle were important to this research:

1) the oviposition time frame; 2) the period of time for eggs to develop into adults; 3) the drying off period before host-seeking; and, 4) host-seeking. Hartley et al. (2012) used two days for oviposition when modeling the effects of temperature on WNV transmission.

Vinogradova (2000) reported the minimum duration from eggs to adult was ten days at

25°C and nine days at 26°C. Madder, Surgeoner, and Helson (1983), in laboratory settings, estimated one to four days for eggs to hatch at 15°C-30°C, 7-12 days, with air temperatures between 20°C-30°C for larvae and pupae development. Between May 1 and

August 19, Madder et al. (1983) estimated four generations of mosquito broods would hatch into adults. The time frame for egg, larvae, and pupae development was temperature dependent and estimated at 19 days for the May brood, 13 days for the June brood, 12 days for the July and August broods, and 14 days for the September brood.

Tachiiri et al. (2006) estimated three days for an emerged adult mosquito to dry before proceeding to host-seeking, which was estimated at one day. Madder et al. estimated four days for mating, nectar feeding and host-seeking.

Kunkel, Novak, Lampman, and Gu (2006) employed field observations to study the effects of temperature on the timing of the relative proportion of Culex pipiens and

Culex restuans. Kunkel et al. (2006) suggested that, with optimum water temperature, larval density and proper nutrients, the time from oviposition and hatching into an adult mosquito is 8-12 days in Illinois. The authors noted that WNV outbreaks “tend to occur when temperatures are above average and precipitation is below average” and “higher temperatures may increase the (ovary) development rate of the vector, decrease the

32 interval between blood meals, and increase the virion replication rate and magnitude of the infection in the vector, thus considerably shortening the EIP” (Kunkel et al., 2006).

1.7.3.2 The effect of WNV titer levels on the transmission of infection between

vectors and avian hosts

In the laboratory-based WNV transmission studies reviewed in Section 1.7.3.1, mosquitoes were exposed to constant avian blood titers to estimate the effect of varying temperatures on the lengths of the EIP and GCs (Anderson et al., 2008; Dohm et al.,

2002; Kilpatrick et al., 2008; Sardelis et al., 2001). This section provides a review of studies that tested the effect of a mosquito being exposed to variable blood titers on dissemination and transmission. Richards, Mores, Lord, and Tabachnick (2007) fed Culex pipiens quinquefasciatus, a Florida mosquito, 106.2 PFU/ml and held them for 13 days at

25°C, 28°C at 30°C extrinsic incubation temperature (EIT), and fed another group of mosquitoes 105.8, 104.4, or 103.7 PFU/ml and held them at 28°C EIT, to assess dissemination and transmission of WNV by RT-PCR and plaque assay, respectively.

Sample size power (to detect WNV infection) was boosted by testing pools of 100-200 mosquitoes. Dissemination was defined as a virus found in the body, but not the legs and transmission was defined as a virus found in body and the legs. Previous field studies in

Florida documented sentinel chicken amplification at 25°C and transmission at 28°C EIT.

Thirteen days were chosen as the time for infectiousness from previous studies, which had described the relationship between EIP and EIT as inverse (the higher the EIT, the shorter the EIP). Richards et al. (2007) found that the EIT had an effect on vector competence. For mosquitoes with a virus exposure of 106.2 PFU/ml and held at 25°C, 28

°C, and 30°C, the IRs were 30%, 52%, and 93%, and dissemination rates were 33%,

33 22%, and 81%, respectively. An additional experiment testing the effects of virus exposure on vector competence for 13 days at 28°C with virus exposures of 104.4, 105.8 and 103.7 resulted in only the 105.8 PFU/ml exposure having a significant effect on vector competence. After a 13 day EIP, there was no significant difference in vector competence between virus exposures of 104.4 and 105.8.

Vanlandingham et al. (2004) was the first study to quantify WNV from Culex pipiens quinquefasciatus saliva, collected in Houston, Texas, which is critical to understanding vector competence, especially relative to the titer concentrations needed for detection of the virus using RT-PCR, and furthering the science on the exposure (titer concentration) needed to infect host species. A group of 300 mosquitoes were fed blood with a titer of 107.9 PFU/ml and maintained at 26°C for 6, 14, and 21 days with a 12 hour/12 hour light:dark cycle. In whole mosquitoes, Vanlandingham et al. (2004) found mean titers for 6, 14, and 21 days at 104.4, 106.4, and 106.3 PFU/ml, respectively. In mosquito saliva glands, 93% of the saliva collected on day 14 post infection (pi) and 81% of the saliva collected 21 days pi had detectable WNV. “The mean titer for 14 and 21 days was ~3x105 and ~6x10 4 PFU/ml” (Vanlandingham et al., 2004). This study documented that Culex pipiens quinquefasciatus can transmit an average of 104.3 PFU/ml of virus, which was important because the WNV titer of the vector not only affected vector competence, but the sensitivity of the testing method used to identify the virus.

1.7.3.3 Avian host, pathogen, and vector transmission cycle dynamics

Arboviruses are maintained in a complex enzootic cycle of least one non-human primary vertebrate host and a primary arthropod vector. Meteorological conditions including temperature and precipitation and vegetative patterns can moderate, modulate,

34 or unleash the enzootic cycle of each arbovirus into a full-blown epizootic.

Epizootics/epidemics occur when this enzootic transmission cycle becomes unbalanced, usually triggered by considerable ecological changes. Competent vertebrate hosts and primary or secondary vectors amplify the virus and transmit it to human or equine hosts

(Andreadis et al., 2004; Kilpatrick et al., 2006; Mostashari et al., 2003; Ruiz, Tedesco,

McTighe, Austin, and Kitron, 2004; Ruiz et al., 2010). Figure 1.6 is an illustration of the

WNV transmission cycle from mosquitoes to bird and human hosts, and includes the

GCs, the EIP and the ecological phases used in this study that are proposed to be necessary for oviposition: oviposition and preoviposition site development.

WNV has been isolated from 65 mosquito species in the U.S. (CDC, 2013). Culex pipiens spp., the subject vector of this study, is both an avian and mammal feeder, and is identified as a bridge vector, because it has “bridged” a gap between these two distinct hosts due to its genetic ancestry. Culex pipiens was first reported as a bridge vector in a publication by Hamer et al. (2008a) resulting from the RT-PCR testing of five blood fed mosquitoes in 2005 for avian or human genetic fingerprints, and one mosquito tested positive for a human blood meal. At least 22 other mosquito species have been identified as epidemic or bridge vectors (Hamer et al.). Two species are formally recognized in the

Culex pipiens complex, Culex pipiens Linnaeus 1758 and Culex quinquefasciatis Say

1823, the northern and southern house mosquito (Fonseca et al., 2004). Fonseca et al. used genetic fingerprinting of eight alleles of 33 groups of mosquitoes from the U.S.,

Europe, North Africa, Japan, the Middle East and Australia, to test two theories of ancestral derivation regarding the existence of two behavioral and physiological approaches to the gonotrophic cycle and oviposition: 1) Culex molestus (now

35 synonymous with Culex pipiens), an underground breeding species not requiring a blood meal (autogeny) before first oviposition; and, 2) Culex pipiens, an aboveground breeding species that requires a blood meal (anautogeny) before first oviposition,

Pupae Adult

Bird Human Host Host

Larvae Extrinsic Incubation Period (EIP) Preoviposition Site Development

Eggs Oviposition Eggs ______to______Hatch______Phase ______to______Hatch______Phase Eggs Oviposition Site Development Figure 1.6. WNV transmission cycle and the mosquito life cycle including proper site development for oviposition the eggs, larvae, and pupae phase (eggs to hatch) and adult phase; feeding on bird and dead-end hosts such as humans; through the gonotrophic cycle between blood meals and oviposition; and through the extrinsic incubation period of the virus, the time between the mosquito being infected to becoming infectious, which depending on ambient temperature and the virus titer of bird hosts, may take as long as one or more gonotrophic cycles

36 to document clustering effects of the species currently classified as Culex pipiens, which began to answer the question of whether these two behavioral approaches were from the same or different species. The results of the genetic fingerprinting showed a clustering of three allelic groups: a northern European aboveground and a northern European underground cluster, with North Africa, Middle East, Japan and Australia clustering with the underground group, and the U.S. groups clustered separately. Further analysis indicated that 40% of the U.S. cluster was a hybrid of the northern European anautogenous and autogenous groups and that Culex quinquefasciatis clustered separately. Because there were more common alleles between the northern European underground cluster and North Africa and Middle East mosquitoes than the northern

European aboveground cluster, the authors concluded that these were two separate species. The result of this hybridization was a new, more efficient “bridge vector” species, which is both a mammal and bird biter, and can transmit the WNV between avian and human hosts.

WNV has been isolated from > 130 non-avian vertebrate hosts (CDC, 2013), and

WNV encephalitis can be fatal in equines, rodents, felines, rats, ungulates (hoofed animals), and humans, among other carnivores, primates, lagomorphs (rabbits), and chiropterans (bats). Humans, equines, and other mammals are dead-end hosts, as they do not amplify and transmit the virus (Arstob et al., 2009; Weaver and Barrett, 2004). In the

Old World, birds and mammals have developed immunity, and they rarely show signs of clinical symptoms. However, within the last ten years their immune status has been compromised, potentially caused by a newer more virulent strain (Weaver and Barrett,

2004). In the New World, an epidemic began in 1999 in New York City, and with host

37 immunity underdeveloped, mortality and morbidity was rampant in birds and vertebrates

(Weaver and Barrett, 2004).

WNV was detected in 336 native and exotic (captive) bird species from 1999-

2012 in the U.S. (CDC, 2013). The spread of WNV rapidly across the U.S. is thought to be the result of long distance movement of migratory birds. To maintain an enzootic cycle, the virus must be transmitted from a competent vector to a competent avian host.

Mortality and morbidity in avian hosts are associated with corvids (Blue Jays, crows, magpies, grackles, and ravens). Robins, jays, and the common grackle have been identified as competent hosts responsible for the dispersal of the virus throughout the

U.S. (Arstob et al., 2009). The American Crow was the ideal WNV sentinel species surveillance system because of its high incidence of mortality, easy recognition by the public and wide distribution of habitats in rural, suburban and urban areas (McLean,

2006). In the 1999 surveillance season in New York City, 89% of the birds testing positive for WNV were American Crows (McLean, 2006). By 2000, the epizootic had moved to 12 northeastern states as far south as North Carolina, and again, 89% of the birds tested positive were crows (McLean, 2006). The one American Crow testing positive in North Carolina in 2000 was probably due to the expansion of the virus southward by migratory birds (McLean, 2006). In 2001, the virus was detected in

Midwestern states (Ohio, Michigan, Wisconsin, Illinois, and Indiana) and as far south as the Florida Keys, with 70% of the dead birds submitted in the U.S. being positive

American Crows (McLean, 2006). Fall 2001 bird migrations from the northern epizootic areas along the Mississippi flyway helped to disperse the WNV along the Mississippi river (except Minnesota) to the Gulf Coast states, advancing the expansion during 2002

38 when, during June of 2002, WNV was detected in birds from North Dakota, Minnesota, to Texas and into the Canadian border, and by the end of the summer of 2002, was found in birds westward to Saskatchewan and northeast to Quebec and Nova Scotia, as well as

Mexico and the Caribbean, strengthening the theory that WNV was being spread by birds along the Mississippi and Central U.S. migratory bird flyways (McLean, 2006). In 2002,

53% of the WNV positive birds in the U.S. were American Crows. In 2003, while the

WNV epizootic continued its westward expansion into the Great Plains and Colorado, the largest epidemic of human cases in history occurred in its wake (McLean, 2006) due to ideal weather conditions (wet spring, hot summer) for mosquito breeding, egg, larvae, and pupae development, and viral amplification resulting in highly competent mosquitoes. The Pacific flyway facilitated the virus in its expansion from Mexico to

Arizona and California in 2003, during which 50% of all birds that tested positive were

American Crows. In the spring of 2004, the WNV was moved from southern to northern

California by migrating birds and amplified, which most likely resulted in the first human cases reported one year after the virus was introduced into the state, and subsequent epidemic-like conditions (McLean, 2006).

Beginning in 2004 east of the Rocky Mountains, WNV activity waned due to changes in climate conditions such as cooler and wetter summers, but weather patterns in

California and Arizona were still conducive to WNV transmission. Cooler weather reduced the efficiency of the virus and bird competence by lengthening the EIP of the virus and GC of the mosquito and decreasing the population of mosquitoes and infected birds and non-avian vertebrate hosts, including human dead-end hosts (McLean, 2006).

It appears counterintuitive to assume that highly susceptible birds, e.g., the American

39 Crow would be considered an important epidemic host. Laboratory tests have found that blood titers in American Crows were highest three to five days before mortality, and sick birds may have been more of a target for a blood meal by the mosquito. Researchers have documented crow-to-crow transfer of the WNV through virus-laden exudates in contact chambers (McLean, 2006). The effect on bird populations has been measured using

Audubon Society Christmas Bird Counts (CBC), although these counts are not sensitive enough to measure high resolution bird population declines, only declines over large areas and large populations. The CBC documented American Crow declines in New

York City presumably resulted from the 1998-1999 epizootic (McLean, 2006). As stated by McLean (2006,) “there are many biological factors associated with vectors, hosts, and viruses that influence the occurrence and frequency of transmission as well as abiotic factors, including temperature and moisture that affect the biological factors...Climate and seasonal weather affect the winter survival, spring initiation (of host-seeking), summer intensity (competence of vectors and hosts), and the intensity of local or regional

WNV transmission” (McLean, 2006).

Kilpatrick et al. (2006) studied host selection of Culex pipiens and the association of selection to the human WNV epidemic curve from field data collected in Maryland and Washington, U.S., in 2004. Using RT-PCR and deoxyribonucleic (DNA) sequencing of Culex pipiens avian blood meals, it was found that 51% of the Culex pipiens blood meals came from the American Robin in May and June, even though the robin only contributed to 4.5% of the avian population. The robin population declined from July to

October, and coincided with an increase in human blood meals, while at the same time the House Sparrow population grew from 42%-67% of total avian abundance, that

40 prompted a conclusion by Kilpatrick et al. that “the shift in feeding from birds to humans is not a result of decreasing avian abundance but is more likely to be the result of the decline in robins, their preferred host.” Kilpatrick et al. did not present any data quantifying the percent of blood meals from other avian species such as the House

Sparrow, which motivated this writer to question the strength of their conclusions.

Host selection by the vector potentially impacts public health due to the competency of the host in viral amplification to become infectious after being infected.

Host competence is a function of the dose and duration of the viral infection in the host, host contact rates, and host survival rates. “Host contact rates are a function of vector feeding preferences and the abundance of susceptible hosts” (Hamer et al., 2009).

Studies have shown that even though Culex pipiens may have a proclivity towards the

American Robin, a.k.a. the “super spreader” of WNV, it is often not the dominant species in the population (Hamer et al.). The American Crow may not be as important a host as once thought, because WNV transmission continued when crow population numbers were reduced due to WNV mortality (Hamer et al.). Hamer et al. designed a field study in

Chicago, Illinois from 2005-2007 to: 1) document whether Culex pipiens selected certain avian species over others, and if these selections affected transmission patterns; 2) test whether American Robins were over or under utilized by calculating a host selection ratio of the utilized birds divided by the available birds in a population; 3) examine temporal shift in feeding patterns; and, 4) estimate the number of infectious Culex pipiens from each bird species to predict the contribution of the bird species to the enzootic and epizootic amplification. Resulting from this research, Hamer et al. using quantitative RT-

PCR found that: 1) 80% of the blood meals taken by Culex pipiens were avian, and 16%

41 mammalian; 2) Culex pipiens fed on 25 avian species with blood meals consisting of 48% from the American Robin, 15% from the House Sparrow, 11% from the Mourning Dove, and 8% from the Northern Cardinal; and, 3) 88% of the mammal blood meals were from humans and 8% were from raccoons. The results of live bird surveillance showed that

House Sparrows were the most abundant (4.25 birds/hectacre), followed by the American

Robin (2.0 birds/hectacre), Mourning Doves (0.63 birds/hectacre), Common Grackle

(0.56 birds/hectacre) and the Common Starling (0.55 birds/hectacre) (Hamer et al.). The host selection ratios showed the American Robin as the only species with a statistically significant overuse ratio (Hamer et al.). An inverse relationship was found between the human WNV epidemic curve from June to August, and the bird feeding of mammals

(including humans) compared to avian feeding, which increased with human cases

(Hamer et al.). The contribution of avian viral amplification to Culex pipiens competency was estimated at 35% from the American Robin, 17% from Blue Jays, 11% from the

American Kestrel, and 5% from the Northern Cardinal (Hamer et al.). Conclusions from this study concurred with the designation of Culex pipiens as a bridge vector, but did not support previous evidence from Kilpatrick et al. (2006) that this mosquito shifts its feeding behavior from birds to humans later in the season during peak epidemic, but on the contrary, showed that Culex pipiens shifted from the American Robin to other bird species when the availability of robins declined (Hamer et al.).

Studies have shown that early season avian deaths were associated with human

WNV infections. In New York City, 2001, dead bird reports, WNV positive mosquitoes, and WNV human cases clusters were identified using geospatial scan cluster statistical analysis in Staten Island, eastern Queens, South Brooklyn and East Brooklyn.

42 Temporally, dead bird clusters occurred 0-40 days prior to the onset of a human illness,

12-45 days before human WNV onset date, and in most cases, prior to collecting WNV positive mosquitoes (Mostashari et al., 2003), with a major weakness identified as not having WNV test data on the dead birds. Guptill, Julian, Campbell, Price, and Marfin

(2003) collected 2001 ArboNET data from 328 counties in the U.S. for WNV infected equines, birds, human, and sentinel animals. Each county that reported WNV positive dead birds were further categorized temporally by whether or not the dead bird report before August 5th preceded at least one human WNV case. A relative risk (RR) statistic was calculated comparing the proportion of counties that had human disease with dead bird reports to the proportion of counties that had human disease with no dead bird reports, and found a RR of 6.43, or the risk of human disease with dead bird reports is increased by 600% over the risk in counties that reported human disease with no dead bird reports (Guptill et al., 2003).

Hamer et al. (2008b) collected birds and mosquitoes in 2005 and 2006 from suburban southwest Chicago and tested for them for WNV using a WNV antibodies test and RT-PCR respectively. Birds were collected with nylon 36 mm mist nets from mid-

May to mid-October on a three-week schedule. Mosquitoes were captured every two weeks at 11 residential and four “green-space” sites with four CO2 baited light traps and four gravid traps. Researchers found that in 2005, the difference between adult bird and hatch-year bird sero-prevalence was significant (24.4% compared to 18.5%, respectively), but in 2006, the difference was insignificant (4.2% versus 2.8%). In 2005 and 2006, the sero-prevalence was 21% and 1% for House Sparrows, 71% and 14% for cardinals, 11% and 4% for American Robins, and 36% and 0% for Grey Catbirds,

43 respectively. Comparing 2005 and 2006 temperature and precipitation, in 2005, temperatures were the 12th hottest on record compared to a cooler 2006 (35th hottest);

2005 precipitation was the third lowest since 1871 and the study area received twice the rainfall in 2006 than 2005. During year 2005, week 30, the Culex spp. IR was twice as high compared to 2006 (59/1000 versus 33/1000 mosquitoes). A statistically significant time lag of two weeks was noted between hatch-year bird seropositivity and Culex spp. infection (writers note: this is the difference in weeks between the week with no positive

Culex spp. infection and the week with its first seropositive birds, i.e., the “no positive

Culex spp. infections week” was two weeks prior to the first week with seropositive birds) for both 2005 and 2006. For total birds, Culex spp. infection was one week prior to bird seropositivity in 2005, and had no time lag in 2006. In 2005 there was a significant association between human cases and Culex spp. infection but had no time lag, and in

2006, Culex spp. infection was significantly associated three weeks prior to human cases

(Hamer et al., 2008b) (note: it was very difficult for this writer to understand the graphics in Figure 5 of the Hamer et al., 2008b study relative to their interpretation in the text.

Figure 5 H appears to be described as 2005 and not 2006).

Foppa and Spielman (2007) explored the effect of local avian host die-off on the perpetuation of an enzootic. Mathematical simulations revealed that this scenario may have been possible with the assumptions that: 1) “mosquito feeding rates are constant; 2) the larger the mosquito host ratio, the more blood meals will be taken on a particular host; 3) incompetent hosts do not divert mosquitoes from competent hosts; and, 4) the mosquito population stays constant” (Foppa and Spielman, 2007). According to the authors, the first assumption may not have been realistic unless there was a constant

44 spatial correlation between the hosts and the vector. Also, for host mortality to affect the perpetuation of an enzootic, there could not have been an influx of alternative hosts to feed upon, and a large percentage of the infected birds would have had to perish. Because the assumptions that were implicit to the Ross-Macdonald expression (modeling formula) used in this study, any modeling results would need evaluation (Foppa and Spielman,

2007).

1.7.4 Detection of WNV infection in mosquitoes

1.7.4.1 Detection methods overview

Vitek, Richards, Robinson, and Smartt (2010) described the “common methods for arbovirus testing of mosquito pools” as “plaque assay, RT-PCR, Rapid Analyte

Measurement Platform (RAMP®), manufactured by Response Biomedical Corporation,

Vancouver, BC, Canada, VecTest®, manufactured by Medical Analysis Systems,

Freemont, CA, and quantitative real-time RT-PCR (qRT-PCR).” Each of these methods has strengths and weaknesses for detecting WNV infection in mosquitoes.

RAMP and VecTest are used by mosquito control programs in lieu of RT-PCR as a quick, sensitive, field-based “bench” test of the presence of WNV in mosquito pools, usually performed the same day of mosquito collection after the mosquitoes are counted, identified, and pooled (up to 50 mosquitoes). RAMP and VecTests give mosquito control managers immediate results compared to the time required for RT-PCR testing, including transporting the pooled specimens to a laboratory, testing the mosquito pools, and communicating the results of testing back to program managers. RT-PCR testing times are a function of staffing, equipment availability, and the number of separate pools to be tested at any point in time from multiple mosquito program jurisdictions. In Ohio, during

45 the epidemic years or 2002-2003, there was a 1-2 week time frame between trapping dates to receiving RT-PCR test results, which was past the point of making effective intervention decisions based upon WNV positive mosquitoes alone. The VecTest is a qualitative immunochromatographic dipstick test for up to 50 mosquitoes, comparable to the assay technology used in pregnancy tests. 250 µL of the supernatant of the homogenized and centrifuged mosquitoes is placed in a 1.7 ml conical-bottom tube with a dipstick. After a 15 minute incubation period, a monoclonal antibody-antigen-colloidal gold complex migrates to a portion of the dipstick and forms a pink-red line; if the control line is visible, the test is interpreted as positive. The RAMP test uses an antibody- fluorescent latex conjugate to react with the WNV antigen. After homogenization and centrifugation, 100 µL of the supernatant is mixed with the conjugated antibody complex, and 70 µL of the sample-conjugated antibody mix is added to a test cartridge. After a 90 minute drying time, the antigen-bound particles are immobilized at the detection zone and control particles at an internal control zone. “The control zone corrects for sources of variability (i.e. membrane variability, environmental conditions and operator technique) to provide an internal validation of the assay” (Response Biomedical, 2013). The cartridge containing the test strip and the sample-conjugated antibody mixture is placed into the RAMP reader, which measures the fluorescent values at each zone. The qualitative and quantitative reading is a ratio of the detection and control zones. A reading of ≥15 RAMP units is considered positive (Burkhalter et al., 2006).

Vero cell assays are a quantitative method to determine the number of PFUs formed by living virus. Vero cells are seeded into cell culture plates at 6.0×105 cells per well and incubated until a monolayer of cells is formed. A clarified supernatant of

46 mosquito virus suspension is made into 10-fold dilutions (10-1, 10-2, 10-3, 10-4, 10-5, 10-6,

10-7and10-8) in preparation for the assay. A final volume of 0.1 ml of 10-fold dilutions of the virus is inoculated on the monolayer of Vero cells and allowed to attach and absorb into the cells. After an absorption period, the monolayer is covered with a gel, usually agar, and incubated for a specified time period. The viral progeny of the original infected cell is released into neighboring cells, and a plaque of infected cells is formed, which can be counted with or without enhanced staining. The titer of the original WNV supernatant

(stock) can be calculated by counting the PFUs per ml. For example, if 18 PFUs were counted on a culture plate made with a 10-5 dilution of virus stock, the virus titer would be 1.8 x 107 PFU/ml. Payne, Binduga-Gajewska, Kauffman, and Kramer (2006) outlined a standard method for standard plaque assays and fluorescent foci assays.

Reverse Transcriptase-PCR is a method used in molecular biology to qualitatively detect gene expression by creating complimentary DNA (cDNA) transcripts from mRNA. RT-PCR takes place in two steps, RT, and PCR. The mRNA WNV virus, once homogenized and extracted from mosquito specimens, is then reverse transcribed using oligonucleotide primers specific to one or more genes by reverse transcriptase, an RNA- dependent DNA polymerase enzyme, into cDNA, which then becomes the template for further amplification of the virus for analysis. The primers bind to the mRNA primer binding site (PBS) and initiate the extension on cDNA from the 3´ to 5´ direction. An aliquot of the RT reaction plus PCR components are cycled through specific time and temperature cycles in a three step process of denaturation, annealing, and elongation to amplify the cDNA. Over one billion copies of cDNA can be produced in 30-40 cycles.

Typically, the products of amplification are stained using ethidium bromide and analyzed

47 by agarose gel electrophoresis under ultra-violet (UV) light. Mans et al. (2004) contains a standard method used in RNA extraction and RT-PCR used by the ODH.

Real-time RT-PCR provides a semi-quantitative analysis of viral amplification in real-time using fluorescent DNA probes such as SYBR green or TaqMan. These two probes act in different manners: 1) SYBR green, a more economical fluorescent dye, binds to each copy of cDNA and emits light upon excitation, proportional to the number of copies, by a UV transilluminator (Oatey, 2007); and, 2) the less economical TaqMan probes are oligonucleotides with a fluorescent probe attached to its 5´ end that attaches to pre-engineered sequences on the DNA template, and when polymerase replicates the

Taqman-bound template, it cleaves off the probe, which then becomes fluorescent. Mans et al. (2004) contains a standard method used in real-time RT-PCR. Using real-time RT-

PCR, different concentrations of cDNA can be measured by documenting the cycle number that produces an exponential increase in cDNA fluorescence above a cycle threshold (CT) set by the researcher above the background fluorescence, usually between

35 and 40 cycles. Background fluorescence is the level of fluorescence emitted without the amplification of the cDNA products emitted by SYBR green in solution. There is an inverse exponential relationship that exists between initial quantity (copy number) of target sequence copies in the reactions and corresponding CT determinations, i.e., the higher the starting copy number of DNA target the lower the CT value. Since the quantity of DNA doubles every cycle during the exponential phase, relative amounts of DNA can be semi-quantitatively estimated, e.g. a sample whose CT is 3 cycles earlier than another's has 23 ≈ 8 times more template (3.3 cycles ≈ 10 times more template) (USCMED, 2013).

48 1.7.4.2 Sensitivity studies of WNV detection methods

Lanciotti et al. (2000) compared the sensitivity of TaqMan to standard RT-PCR and found that both assay methods detected 1 PFU of WNV, but standard RT-PCR was

10-fold less sensitive. Also, RT-PCR was 1000 times less sensitive at detecting the virus in pools of mosquitoes.

Lampman, Krasavin, Szyska, and Novak (2006) compared TaqMan RT-PCR with the VecTest assay, and found in 2002 that in two different mosquito abatement districts, true positives and false negatives for VecTest were 47.7% and 43.5% and 26.9% and

31.5%. The increase in the proportion of false positives in 2003 and 2004 was thought to be the result of mosquito IRs of < 1/1000. Sutherland and Nasci (2007) tested the RAMP, and VecTest assays, real-time RT-PCR, and Vero cell assays for sensitivity in large pools of mosquitoes (up to 500 mosquitoes per pool) and found “A single mosquito infected with WNV can be detected reliably in mosquito pools containing up to 200 specimens with all 4 of the assays we evaluated (Vero cell plaque assay, Taqman real- time RT-

PCR, RAMP and VecTest).” Titers in the positive pools were 5.4-6.8 for RAMP, 6.2-6.9 for VecTest, 2.9-5.4 in TaqMan real-time RT-PCR (for pools of 500), and 0.9-3.1 in Vero plaque assay. To test the sensitivity of RAMP and VecTest on variable size pools of 50,

100, and 200 pools, a titer of 9.9 log10 PFU/ml of virus in 10-fold serial dilutions was seeded into the grinding medium of each test. Pools of 50, 100, and 200 negative mosquitoes were added to the respective mediums. The RAMP test detected the virus below 3.3 log10 PFU/ml in samples of 200 mosquitoes, and the VecTest down to 5.2 log10

PFU/ml in samples of 200 mosquitoes, but not lower, regardless of pool size. In another experiment, a pool of 500 homogenized mosquito bodies was tested against a mosquito

49 free pool of RT-PCR tissue grinding medium. Both pools were then inoculated with 6.9 log10 PFU/ml of WNV and tested by RT-PCR. CT values for the mosquito free and mosquito body pools were 17.9-18 and 19.1-18.8 respectively. The estimated loss in detection sensitivity due to large amounts of mosquito bodies was 0.6 log10 PFU/ml, or virtually negligible (Sutherland and Nasci, 2007). Burkhalter et al. (2006) tested the specificity of the RAMP and VecTest on RNA extracted from prepared laboratory WNV seed virus, amplified with TaqMan real-time RT-PCR to a high titer, and found that the

RAMP test was sensitive to 3.17 log10 PFU/ml (≥ 15 RAMP units as the cutoff) at CT values as high as 25.9. The VecTest performed with sensitivity to 5.17 log10 PFU/ml (CT

= 19.36). A sensitivity test of RAMP and VecTest was performed on field-collected and homogenized pools of mosquitoes The RNA was extracted and amplified using TaqMan real-time RT-PCR (pools were considered positive at ≤ 37 CT) and it was found that “of the 100 pools that tested positive by RT-PCR, the VecTest detected evidence of WNV in

65 pools and the RAMP test detected evidence of WNV in 94 pools,” (Burkhalter et al.,

2006) with a false positive rate of 6% for RAMP and 35% for VecTest. The RAMP

WNV test detected WNV in pools with RT-PCR CT values ≤ 36 (the highest CT value in the positive samples used in this assay). The VecTest WNV assay could not detect the virus in pools with RT-PCR values > 30 (Burkhalter et al., 2006). Williges, Farajollahi,

Nelder, and Gaugler (2009) compared RT-PCR to RAMP by testing field-caught mosquitoes using the manufacturer recommendation of 30 for the RAMP unit cutoff. The results were reported as: “we assayed 431 mosquito pools for WNV using both RAMP and RT-PCR for each pool. Overall, 316 pools tested WNVnegative and 115 pools tested

WNVpositive using both systems. Eighty-nine pools tested positive using RAMP, and all

50 were confirmed by RT-PCR. An additional 26 pools were WNV negative using RAMP but WNV positive using RT-PCR. The time required to obtain results from mosquito assays was on average two to five hours for RAMP and seven to ten days for RT-PCR”

(Williges et al., 2009). This writer questioned a conclusion from Williges et al., which stated that “using RAMP, we detected WNV in 94% of pools when compared to RT-PCR during our assays of field-caught mosquitoes,” when 89 pools tested positive using

RAMP and 115 pools tested positive using RT-PCR and RAMP, which calculates to

77%. The conclusion could have been stated as “congruence (both RAMP and RT-PCR positive or both RAMP and RT-PCR negative) was found to be 94%,” which is calculated by: [true positives (89) + true negatives (316)] ÷ [total # of pools (431)] =

94%.

Kesavaraju et al. (2012) in a novel study reviewing the results of Burkhalter et al.

(2006), Williges et al. (2009), and others surveyed the sensitivity of 4,759 RAMP tests with cutoff values of > 30 used by researchers and mosquito abatement districts (MADs) compared to RT-PCR results. For all RAMP tests, true positives, false positives, false negatives, and true negatives were 15%, 14%, 4%, and 67% respectively. When considering only positive RAMP tests, 51% were true positives and 49% were false positives (Kesavaraju, et al.). Congruence (both RAMP and RT-PCR positive or negative) was achieved in 82% of RAMP tests using > 30 as a cutoff for positive while

18% of the results were incongruent. The manufacturer recommended cutoff is ≥ 30.

Different cutoff values adopted by MADs due to incongruence were termed “grey zones.” Grey zones have been linked to the variability in preparation techniques across jurisdictions, and have been further complicated by some jurisdictions not confirming

51 positive RAMP tests with RT-PCR. Consequently, the manufacturer suggested that jurisdictions experiencing grey zones use a cut-off value of > 80 as an interpretation of a positive and from 30 to 80 should be confirmed with RT-PCR. Using this recommendation as the hypothesis for the study, Kesavaraju et al. applied the manufacturer cut-off of 80 (any test that was < 80 was negative) to the same dataset of sample results and found 89%/11% congruence/incongruence respectively. For all RAMP tests using 80 as a cutoff, true positives, false positives, false negatives, and true negatives were 13%, 5%, 6%, and 76% respectively. When considering only positive

RAMP tests, 72% were true positives, and 28% were false positives. An analysis of the correlation between CT and RAMP values found a significant correlation between mean

2 CT for every 100 RAMP units (r = 0.80), but found that individual RAMP and CT values were not as strongly correlated (r2 = .34).

1.7.5 Estimation of WNV IRs in mosquito populations

The science of estimating IRs in mosquitoes has advanced since the U.S. WNV epizootic. An accurate estimation of infection in mosquitoes is fundamental to the prediction of human WNV risk from biotic drivers. The estimation of IRs is affected by a complex combination of surveillance methods, biotic and abiotic conditions, and species- specific mosquito biology and physiology: 1) mosquito density, 2) the sample size (the sample of mosquitoes from one trap from which pools of mosquitoes are constructed for testing), 3) pool size (equal or unequal), 4) pool screening method, 5) the estimated IR in the mosquito population from which the sample is collected, 6) sampling methods, 7) mosquito sample segregation or aggregation before pooling for testing, 8) the hazard status of WNV transmission within the mosquito (infected or infectious), 9) IR

52 calculation methods, 10) detection probability of infection in mosquitoes, 11) temporal and spatial variation of WNV transmission in the mosquito, 12) virus quantity in pool,

13) assay detection limits, 14) incubation temperatures of virus (length of the EIP), 15) virus species, 16) mosquito age, 17) mosquito survivability, 18) mosquito species, 19) number of gonotrophic periods (blood meals) (Bustamante and Lord, 2010), and, 20) avian host species, availability, and competence (McLean, 2006).

One of the most critical decisions that may affect the estimation of human WNV risk is the calculation method used to estimate IRs in mosquitoes. The three main methods that have been presented in the literature are MIR, MLE, and density of infected mosquitoes (DIM). MIR is estimated by the following formula: the number of positive pools divided by the number of total mosquitoes in the sample times 1000. The MLE is an estimate of the actual number of positive mosquitoes in a sample (using PooledInfRate software as an add-on to Excel, Biggerstaff, 2003). Both MIR and MLE are expressed in

IR/1000 mosquitoes. DIM is an estimate that is a function of mosquito density and the

MLE (Bustamante and Lord, 2010; Gu, Unnasch, Katholi, Lampman, and Novak, 2008).

MLE as defined by Gu et al. (2003) is “the infection rate most likely observed given the testing results and an assumed probabilistic model (i.e., binomial distribution of infected individuals in an infected pool).” Myung (2003) described maximum likelihood estimation “as a method to seek the probability distribution that makes the observed data most likely.”

In a letter to the editor, Gu et al. (2003) reiterated the underlying assumption of the MIR metric as “only one infected individual (mosquito) exists in a positive pool.”

MIR estimates the lower bound of the IR and the MLE, which relaxes the assumption of

53 the MIR, estimates the number of infected individuals in a sample. Gu, et al. did not support the suggestion by some authors that a sample size of > 1000 mosquitoes was needed for an accurate estimation of MIR or MLE, and stated that the MLE estimate was more accurate and robust than the MIR. The MLE has existed since the early 1960’s but has not been recognized by medical entomologists (Gu et al.).

Sample size, pool size, and IRs in the mosquito population are the main drivers of the choice of estimation calculation methods between MIR and MLE. When IRs are high

(> 1/1000) and pool sizes large (≥ 50 mosquitoes per pool), MIR underestimated true IRs.

“When infection rates are low and/or pool sizes are small, MIR provides good estimates of the true infection rate because the chance of more than one infected mosquito in a positive pool is negligible” (Gu, Lampman, and Novak, 2004). Under these conditions, the estimates generated for MIR and MLE are typically very similar (Gu et al., 2003).

The accurate estimation of IRs using “optimum” pool sizes based upon a prior estimate of

IRs have been a subject of past studies. The accuracy of these estimates was dependent on the accuracy of the prior estimate and was not reliable due to the spatial and temporal variations in viral infection, especially during epidemics of emerging diseases (Gu et al.,

2004). In areas of high WNV infection, emphasis should be on the estimation of infected individuals (using MLE), whereas in areas of low infection, it is more important to focus on collecting and testing as many individuals as possible to increase the probability of finding infected individuals.

Simulations were performed to test the accuracy of variable and constant size pooling methods by randomly assigning “infectivity” to individual mosquitoes and pooling the individuals in 12 sequentially sized pools, (5, 10, 20, 30, 40 and 50) and 12

54 pools with a constant pool size of 50 individuals. Generally, MLE was found to be positively biased and MIR was found to be negatively biased compared to the true IR.

When IRs were below 10/1000, both MIR and MLE were accurate estimates. MLE was closer to the true IR than MIR with sequential pooling. When the true IR was > 30/1000 individuals, MLE became very positively biased and the MIR capped at 20/1000 (12 positive pools divided by 600 individuals times 1000) when all pools tested positive with an increase in the true IR and constant pool sizes of 50 mosquitoes (Gu et al., 2004). Gu et al. concluded that with sequential pool sizes, the same number of pools, and fewer mosquitoes than constant pooling, better estimates were simulated. One disadvantage of

MLE with sequential pooling when compared to MIR, according to Gu et al., was that with only one infected individual in a sample of mosquitoes collected when IRs are low, this infected mosquito could be placed in a pool of five or a pool of 50 mosquitoes and will result in different estimates of infection (with MLE estimate), whereas the MIR would be the same (1 positive pool divided by 310 individuals times 1000 = 3.23/1000).

Gu et al. iterated that the disadvantage when using MIR to estimate IRs was the lack of comparability between MIRs “when pool size and infection rates are not consistent

(across samples)” because the assumption of one positive individual per pool becomes invalid when IRs are high and pool sizes are large. Gu et al. summarized by stating that during an epidemic, when IRs are high, employing resources to ensure large sample sizes is not necessary. “An important measure of better pooling is to have small proportions of positive pools when transmission is high or epidemic” Gu et al. (2004), i.e., to have some negative pools.

55 Gu and Novak (2004) focused on the sample size needed to increase the probability of detection of positive mosquitoes, particularly early in the season when IRs are low. To calculate the minimum sample size needed in order to attain a certain probability of detection, given the IR, the following equation was used: where P is the probability of detection, N is the sample size, and r is the IR,

For example, at an IR of 1/1000, a sample of 970 mosquitoes would equate to a probability of detection of 0.60, and a sample of 370 mosquitoes would result in a probability of detection of just under 0.30. At an IR of 1/1000, approximately 1600 mosquitoes are needed to attain a detection probability of 0.80, considered a high power of detection (Gu and Novak, 2004). To increase sample size, mosquito control managers may consider aggregating mosquitoes from several sites to increase their probability of detection, in order to estimate early-season IRs. However, Gu and Novak (2004) warned that this grouping strategy may not provide the information needed to estimate IRs in foci of transmission, and suggested a separate analysis from each sampling site unless there was homogeneity of IRs across all sites. In cases where sampling area IRs are heterogeneous and sample sizes at some sites are not large enough for a “reasonable probability of detection,” Gu and Novak (2004) suggested that cost-effective surveillance methods be developed to prioritize intensified sampling in only those “sentinel sites” where there was a history of human WNV cases, positive dead birds, and high densities of mosquitoes with positive mosquito pools, which could result in large samples of mosquitoes and an acceptable probability of detection of low IRs.

Additional literature needs to be recognized as essential to making decisions regarding the methods used to estimate IRs besides the method of calculating MIR or

56 MLE, and considering sample size, pool size, and probability of detection. Katholi and

Unnasch (2006) provided insight to the following questions: 1) How many insects should be included in each pool? 2) How many insects should be screened? 3) How should pools be constructed? 4) How many insects do I need to screen to get an accurate estimate of the prevalence of infection? and, 5) What is the meaning of a confidence interval in a pool screen calculation? The three overarching approaches to screening individual mosquitoes for WNV were described as screening each mosquito individually, screening pools of equal size, and screening pools of variable size, the very latter most likely used when collecting arboviruses, where spatial and temporal variation in density is common.

Each screening method determines the probability model for the number of positive pools, e.g., as described earlier, with equal size pooling the probability model is the binomial distribution. MLE has been extended to estimate IRs in samples with variable size pools, by computing within a “generalized linear model framework using a binomial model and log link” (Biggerstaff, 2003), which requires iteration with specialized computer programming.

Various screening methods have been introduced in the literature. Gu et al.

(2004) suggested that when IRs are high, pools sizes may be adjusted using a sequential pooling method that reduces pool sizes until some pools are negative (so that they are

“not all positive”), which, when applied to real-time surveillance, could cause delays in pooling individuals for testing while awaiting the results from previous tests. Katholi and

Unnasch (2006) proposed a “sequential screening” method where the investigator screens pools of equal size until R positive pools each containing K insects are obtained, which was also labeled “inverse sampling” by Pritchard and Tebbs (2011), not to be

57 confused with “sequential pool sizes” as described earlier by Gu et al. (2004). In sequential screening, only a portion of the samples of individuals are screened using equal pool sizes. The probability distribution of the number of positive pools in the method proposed by Katholi and Unnasch (2006) was a negative binomial model and only supported the use of equal pool sizes, whereas the binomial distribution supported screening the entire sample with equal or unequal size pool sizes. Katholi and Unnasch

(2006) summarized by stating that whether IRs are calculated using unequal or equal pool sizes on the entire sample collected or equal pool sizes on a portion of the sample, a critical assumption, “the collection screened must represent a truly random sample of an essentially infinite population” of mosquitoes” must be met.

The sequential screening method proposed by Katholi and Unnasch (2006) prompted Pritchard and Tebbs (2011) to test a new pool screening method, because they believed that sequential screening would be best applied to epidemics where high IRs and the intensity of transmission reduced the number of pools that needed to be tested to obtain a positive pool, and consequently reduced costs. With low IRs, this method became problematic since the number of pools observed to achieve R positive pools was not fixed, and hence it may take a considerable number of pools and resources (funding) to achieve the desired results (Pritchard and Tebbs, 2011). Pritchard and Tebbs (2011) explored the bias in estimators when R is small and the asymptotic assumption may not apply by testing four additional estimators: “shrinkage,” “shift,” “combined,” and

“jackknife,” then compared those estimators to MLE (which is biased when pool numbers are small) (Katholi and Unnasch, 2006), and found that when the true value of the parameter (the true but unknown IR) is < 10/1000, the estimators were better than the

58 MLE. These estimators were posted at the following website: http://r-forge.r- project.org/, under the name invbingroup.

Mosquito control managers may consider devising and implementing a screening strategy whereby only a randomly selected portion of the total sample of mosquitoes collected are used for screening. Katholi and Unnasch (2006) noted that this strategy is appropriate only if meeting the assumption that the original sample and any sub-sample are drawn from an infinite population of mosquitoes and that the sub-sample is “small compared with the insect population present.”

Katholi and Unnasch (2006) considered two measures of importance when answering the questions: 1) How many insects should be included in each pool? 2) How many insects should be screened? 3) How should pools be constructed? These measures were biased, defined as the difference between the expected value of the estimator of the

IR and the true value of the parameter, and error (Mean Square Error), defined as the squared difference between the expected value of the estimator and the true value of the parameter, with the ideal estimator having zero bias and minimum error, which is difficult to achieve. Bias was estimated as increasing with an increase in pool size and its size was influenced by the increase in the unknown parameter value. As the number of pools, M, increased for any pool size, bias was estimated to decrease at a rate of 1/M

(Katholi and Unnasch, 2006). Pool screening (compared to screening individual insects) resulted in an upwardly biased point estimate (overestimation) of IR, but when the true

IR was low, this bias was negligible. Across pool sizes from 1-100, the range of confidence intervals of the expected values of the estimator were about four-fold, and increased only by 9%, that was interpreted by Katholi and Unnasch (2006) as an

59 indication that the estimator error was more likely caused by random error during the sampling process rather than pool screening methods.

To answer the question of how large pools should be, Katholi and Unnasch (2006) noted, as pointed out by Gu et al. (2004), that if pools are too large they will likely test all positive, especially when IRs are high, and if pools are too small (with low IRs), they will likely test negative, wasting money and resources. When the IR is low, assay specificity becomes more important to prevent false negatives or false positives (Katholi and

Unnasch, 2006). Ultimately, the statistical model which supports each screening method

(e.g., binomial, negative binomial) and the assay method used to test (screen) pools of mosquitoes, determines the pool size. Studies have proposed formulas to determine pool

sizes based upon IRs. One such formula, , where p is the IR,

calculates that a pool size of 277 is needed with an IR of 2.5/1000, but is highly unlikely to be tested due to assay constraints. This should not be confused with sample size estimations based upon the probability of detection presented by Gu and Novak (2004).

To answer the question of pool size Katholi and Unnasch (2006) concluded that it was assay dependent and should be as close to 100% specificity and sensitivity as possible.

The next question answered by Katholi and Unnasch (2006) was: How many insects need to be screened to get an accurate estimate of the prevalence of infection?

The question refers the number of insects collected from a trap such as gravid trap or the

CDC light trap, which are then pooled for screening. Katholi and Unnasch (2006) calculated the probabilities of detecting one infected individual when a certain number of insects are tested, where x is the number of individuals collected before finding the first positive insect, z is the number of individuals examined, and p is the IR, using the 60 formula: . For example, if the IR was 1/1000 and 40 insects were examined, then the probability of detecting one infected individual was determined to be 0.04.

The following formula was used to calculate how large the pools should be as a function of the IR where K is the pool size: .

For example, if the IR is 1/1000 and 1000 insects were screened in 40 pools of size 25, the probability of detecting an infected individual was determined to be 0.63.

To answer the question regarding how pools should be constructed, Katholi and

Unnasch (2006) stressed the importance of attempting to meet two important assumptions: 1) random sampling by randomly distributing traps throughout the area being studied; and, 2) independence between infected insects (each infected insect collected is not influenced by the another insect’s infection status), which is not easily met in some areas of high infectivity and transmission where the nature of an epizootic, hosts and vectors interact with each other. To overcome this problem, it was suggested by

Katholi and Unnasch (2006) to aggregate all of the insects collected into one mega- sample and to randomly create pools of mosquitoes from this sample, which, as cautioned by Gu and Novak (2004), would not result in the resolution needed to identify foci of transmission. The last question answered regarded the meaning of a 95% confidence interval (CI) for point estimators of IRs, which was described by Katholi and Unnasch

(2006) as 95% of the time the CI constructed from the point estimate, would cover the true value of the parameter (the true IR).

Bustamante and Lord (2010) presented an approach to the topic of sources of errors in the estimation of mosquito IRs from the perspective of the analysis of the

61 assumption that when IRs increase, so does the risk of transmission of WNV to human and other hosts species. Evidence was cited to support the assertion that because changes in temperature modulate the response of the mosquito to the virus, this variation inhibits our ability to estimate the risk of transmission of infection, because the proportion of infected and infectious mosquitoes is not known at the time mosquitoes are collected for testing, which is simply a positive or a negative for the presence of the virus, and not the infection status of the mosquito. Trapping (sampling) methods introduce bias into the estimation of IRs because each method targets different stages in the mosquito’s development cycle (host-seeking females trapped by CDC light traps and gravid females trapped by gravid traps). The species of mosquito caught, sample size, and the proportion of infected and infectious mosquitoes are all influenced by the trapping method employed

(Bustamante and Lord, 2010). For example, when analyzing IRs across the state or across counties, sampling bias needs to be taken into consideration when interpreting results if the “brew” (used as attractant in the gravid trap) recipe and preparation are not standardized and concentrations of the final product are not consistent. Another bias is introduced by the sensitivity of the assay method relative to the virus titer in the mosquito, which is dependent upon the mosquito’s host contact rate, host competence, host survival, host species and most importantly, temperature. The assay method may not be sensitive to low concentrations of virus due to low virus titers in the mosquito or mosquitoes tested (Bustamante and Lord, 2010).

The main focus of the research presented by Bustamante and Lord (2010) was to acknowledge that even though associations exist between mosquito IRs and human WNV

(e.g., studies have shown that an MIR ≥ 4 and an MIR ≥ 5 were associated with an

62 increase in human cases), there were limitations and sources of error that needed to be acknowledged, studied, and quantified. These limitations are threefold: 1) a lack of detection does not necessarily mean that the virus is not present in the sample; 2) RT-

PCR testing for RNA virus will detect both an unviable and viable virus; and, 3) infected mosquitoes (that are not infectious) that produce a positive RT-PCR test result may not translate to a human WNV disease risk (Bustamante and Lord, 2010). The research questions asked by Bustamante and Lord (2010) focused on whether the proportion of infectious mosquitoes increases with the proportion of infected mosquitoes and whether reliable estimates of IR reflect the changes in proportion of infected mosquitoes. Two models were developed: model 1) the effect of incubation temperature, mosquito survival, mosquito species, and virus species on the relationship between the proportion

of infected and infectious species using the equation: “where p is the daily survival rate of the mosquitoes, pt the proportion of mosquitoes surviving after t days, INFt is the proportion of mosquitoes at time t that carry the virus, Dt-t1 is the percentage of mosquitoes that show dissemination after t-t1, days of incubation under a particular temperature, and t1 is the first and only blood meal” (Bustamante and Lord,

2010); and, model 2) how sampling, pooling, and testing methods affected the

“relationship between the proportion of infected mosquitoes in the population and estimated proportions calculated from field samples” (Bustamante and Lord, 2010). The methodology for model 2 consisted of a population of 100,000 mosquitoes, four distributions of IRs (1/1000, 5/1000, 10/1000 and 15/1000), two virus titer distributions of mosquitoes (one with a low WNV blood titer and one with a higher WNV blood titer),

63 two sample sizes (200 and 2000), two pool sizes (20 (small) and 50 (large)), and two assay sensitivities (low and high).

The finding exhibited by Model 1 suggested that temperature modulated the days to dissemination (when mosquitoes became infectious) in two different mosquito species cohorts. Culex pipiens quinquefasciatus became infectious an estimated 23.5 days after initially becoming infected (includes 3.5 days from emergence to first blood meal) at

20°C and by the fifth blood meal, and approximately 13.5 days and the fourth blood meal at 20°C. Bustamante and Lord (2010) deduced that “the peaks of infection and infectiousness do not coincide.” Resulting from Model 2, the MLE for the low titer mosquito distribution simulation fell far below the true population IR, and when low titer mosquitoes were simulated with a low sensitivity assay, the assay method failed to detect positive pools of mosquitoes. When high titer mosquitoes and highly sensitive assays were simulated, the MLE estimate fell closer to the population IR, but with an underestimation. When high titer mosquitoes and highly sensitive assays were simulated, the MLE was closer to the true value when the population IR was 5/1000. For the sample size of 2000, the MLE increased with the population values when the simulation was combined with highly sensitive assays (Bustamante and Lord, 2010). The results of

Model 1 illustrated the difficulty in correctly interpreting human health risk from IRs when it is not known at the time of collection whether the cohort of mosquitoes

(mosquitoes hatched at approximately the same time) are on their first, second, third, fourth, or if they survive, their fifth blood meal. This cohort of mosquitoes would have the same IR after the first blood meal as they would after becoming infectious after two or three more blood meals, depending on the temperature. There is also temporal

64 variation of IRs to consider when sampling from the same mosquito biological/ecological system, due to mosquito survival and temperature differences. Interpreting similar IRs and assessing human health risk from two biologically and ecologically different mosquito systems (usually determined by sampling location) should be made with caution because the biology and ecology of each system determines the dissemination of the virus (infectiousness of the mosquito). To further complicate the risk assessment process, the population of mosquitoes sampled usually contains more than one cohort of different ages, blood meal status, and infected / infectious proportions (Bustamante and

Lord, 2010).

When making human health hazard characterizations based upon IRs,

(Bustamante and Lord, 2010) recommended the following: 1) understanding how biases accumulate throughout the entire estimation process; 2) establishing a baseline estimated

IR to determine if increases in IRs occur prior to the occurrence of human WNV cases; 3) analyzing other mosquito and environmental variables such as the relative density of parous females, changes in overall mosquito density, dead bird detection, sentinel chicken seroconversion, temperature and rainfall patterns; 4) using new measures of risk such as, a) the DIM , which is the product of estimated infection prevalence (MIR or

MLE) and mosquito density, named “exposure intensity” in Gu, Lampman, Krasavin, and Berry (2006), b) “the probability that a mosquito species will infect a mammal, which is calculated as the product of density, proportion of blood meals taken from mammals, the estimated infection rate, and the fraction of mosquitoes that will subsequently transmit virus by bite” (Bustamante and Lord, 2010), and, c) the vector index, which “considers multiple species and is calculated as the summation of the

65 product of the average number of mosquitoes of each species per trap times the proportion of infected mosquitoes per species” (Bustamante and Lord, 2010); 5) understanding how the transmission rates of a virus can change when multiple mosquito vector species in a biological/ecological system are considered together; 6) considering that different strains of a virus could vary in their dissemination rates, and be affected by temperature changes; and, 7) studying the effect of breeding (rearing) conditions “on the susceptibility of mosquitoes to become infected or infectious” (Bustamante and Lord,

2010).

Gu et al. (2008) provided surveillance methodologies which would improve effectiveness and efficiency, proposed sampling strategies, and suggested risk estimation methods. Gu et. al believed random sampling strategies were overrated, even though

“statistically sound for generalizability,” and instead recommended a more targeted approach to sampling because random sampling offered a low probability of detection in areas where the IRs were low. Gu et al. advocated for targeted sampling of “hot spots” to assist in early detection, which could lead to earlier interventions to weaken a commencing enzootic. “Enhanced efficiency of early detection” was given as the reason for targeted surveillance defined by Gu et al. as “increased mosquito sampling at sites where interactions between vector mosquitoes and reservoir hosts are favorable for a greater likelihood of arboviral transmission.” To illustrate the efficiency of targeted sampling, Gu et al. described a scenario that would be applicable to a number of the mosquito control programs in the state of Ohio with multiple trap sites scattered throughout their jurisdiction. For instance, a mosquito control program manager has 30 sites and has four staff members visit those sites once per week, and has the specimens

66 back to the laboratory by 10:00 AM, for a total of 30 trap nights per week. For purposes of this scenario, 50 mosquitoes were trapped each night. If, early in the season, only three of these sites were foci of transmission with an IR of 1 per 1000, then using the formula referenced by Gu and Novak (2004) and Katholi and Unnasch (2006),

, the detection probability would be or 0.14. However, if four trap nights per week were dedicated to surveillance at each of the three foci sites, half of the personnel and resources could be used (depending on the distance between foci) to obtain a detection probability of or 0.45, over three times the detection probability from surveillance at all 30 sites.

Gu et al. (2008) laid the groundwork for the discussion of an IR estimation method by iterating the recognized descriptions of mosquito infection, corresponding to the part of the mosquito tested: 1) IR (whole mosquito body), 2) dissemination rate (legs), and 3) transmission rate (salivary glands or heads). When testing the whole body, Gu et al. emphasized that the IR was being used as a surrogate for transmission rate, “assuming the two quantities are correlated,” similar to the use of E. coli as an indicator of the presence of pathogenic bacteria in the analysis of water quality. Gu et al. calculated the domain in which MIR is valid at ≤ 2/1000 when the pool size is 50 and ≤ 3.5/1000 when pool size is small (30 mosquitoes), and suggested the use of the MLE as an alternate estimator. To estimate the risk of human exposure, Gu et al. corroborated with the suggestion by Bustamante and Lord (2010) and Gu et al. (2006) to use of DIM as an estimator, but substantiated the DIM with the equation where P is the probability of exposure, m is the mosquito biting rate per person, and r is the mosquito

IR, and re-emphasized that “the use of infection rates alone is not sufficient and can be

67 misleading.” An example was given to illustrate this point; if there were two sites, one with a sample of 500 mosquitoes and an IR of 1/1000, and the other site had a sample of

100 mosquitoes with an IR of 5/1000, the DIM of 50 infected mosquitoes per week was the same for each site, but the IRs were substantially different Gu et al. (2008). Gu et al.

“advocated the use of DIM for most surveillance programs because it is the quantity measuring the frequency of local exposure to arboviral transmission. We believe that the thresholds of (the) risk (of) exposure can be estimated by accumulating DIM data for various vector species in relation to human clinical cases.”

Lastly, Gu et al. (2008) addressed the aggregation of data, acknowledged by

Katholi and Unnasch (2006), and agreed with Gu and Novak (2004) that since the transmission of arboviruses depend on the “intimate interactions between viruses, mosquitoes, and hosts,” the aggregation of data across sites and for long periods of time

“tends to obscure the signature of focal transmission” and the association of these foci with human WNV cases. Gu et al. (2008) believed that the foci should be the unit of analysis and reporting, e.g., in Ohio, Gu et al. would expect each mosquito control program to report surveillance results by foci, which is a biological and ecological system that, given the optimal conditions, has the potential to promote the transmission of arboviruses and create an increase in risk for human WNV disease. Foci-based reporting means that trap numbers and associated pool numbers from that trap would both need to be identified with the foci of transmission, whether that foci is a township, a portion of a community, or a neighborhood. This expectation, although foci-based, is not similar to

“targeted sampling” as described for use in the early season during low transmission.

Chapter 2: Environmental and ecological conditions in Ohio and the temporal and spatial characterization of patterns of West Nile Virus mosquito infection and human disease for

period 2002-2006

2.1 Abstract

Temporal and spatial descriptive analyses were completed on West Nile Virus

(WNV) human case onsets, mosquito infection, dead bird density, and meteorological conditions from 2002-2006, to characterize patterns that will inform future statistical studies. Meteorological patterns from the beginning of the mosquito season that appeared to be related to peak mosquito infection rates (IRs) in Ohio, 2002, were: 1) weekly mean temperature (T) increases from 21°C-26°C during the gonotrophic (GON2) time-delayed index, from 24°C-25°C during the eggs-to-hatch (E_H3) time-delayed index, and an average of 2°C overwinter (December, January, February); 2) Palmer Drought Index

(PDI) decreases from -1.25 to -2.75 during oviposition site development (OVPSD and

OVPSD1) and from + 0.5 to -2.0 during the preoviposition site development (POVPSD and POVPSD1) time-delayed indices; 3) T increases from 15°C-24°C during OVPSD and OVPSD1 and from 15°C to ~21°C during the POVPSD and POVPSD1 time-delayed indices; and, 4) weekly cumulative precipitation (CP) ~ four times higher during

POVPSD and POVPSD1 compared to the OVPSD and OVPSD1 time-delayed indices.

69 In 2002, the documented human case onset epidemic peak was preceded one to two weeks by reported mosquito infection rate peaks, three to four weeks by peaks in documented bird deaths, and five to six weeks by peak reported mosquito density. The characterization of these patterns may provide insights to mosquito control managers as they contemplate the timing of their control strategies.

2.2 Introduction

WNV belongs to the Flaviviridae family of arboviruses (arthropod-borne viruses), which are distributed worldwide. Dengue and Yellow Fever belong to the same family, as do St. Louis Encephalitis (SLE) and Japanese Encephalitis (JE). Arboviruses are maintained in a complex enzootic cycle of least one non-human primary vertebrate host and a primary arthropod vector. Ecological conditions, particularly the relationship of the vector, host, and pathogen with temperature, precipitation and vegetative patterns can both mediate or unleash the enzootic cycles of each arbovirus. Epizootics and epidemics occur when this enzootic transmission cycle becomes unbalanced, usually triggered by ecological changes. Competent vertebrate hosts and primary or secondary vectors amplify the virus and transmit it to human or equine hosts (Andreadis, Anderson,

Vossbrinck, and Main, 2004; Gubler, 2001; Kilpatrick, Kramer, Jones, Marra, and

Daszak, 2006; Mostashari, Kulldorff, Miller, and Kulasekera, 2003; Ruiz, Walker, Foster,

Haramis, and Kitron, 2007; Ruiz et al., 2010). WNV disease in humans causes systemic febrile illness, meningoencephalitis, and death. About 0.66% of people infected develop severe illness; 20% of people infected develop flu-like symptoms; and, 80% of people infected will be asymptomatic (Centers for Disease Control and Prevention, 2013).

70 These data are for those known to be infected; a percentage of the population may be infected without knowing.

Ohio mosquito program managers in local and state health departments and local mosquito control districts had two years from the WNV epidemic in New York City in

1999 to plan for the epizootic and epidemic. In 2001, the Ohio Department of Health

(ODH) began testing dead birds and adult mosquitoes for WNV. By 2002, Ohio was experiencing a full-scale WNV epidemic and epizootic. To prepare for and manage this re-emerging arbovirus, Ohio’s local health departments and mosquito control districts either developed new mosquito control infrastructure or redeveloped and bolstered existing infrastructure and funding. While practitioners, researchers, and state and local public health agencies redesigned shelved mosquito-borne disease programs, state-level policy makers and legislators adopted funding strategies.

Depending on local support, Ohio health departments and mosquito control districts serving counties, cities, townships, and villages funded various levels of mosquito surveillance and control throughout the state, which in some areas, may have effectively reduced disease incidence. The newly redeveloped mid-20th century mosquito control programs included integrated pest management (IPM) methods safer to humans and the environment, that would include public education, surveillance, source reduction, larval control, and adult mosquito control, using chemicals sprayed from truck-based ultra-low volume (ULV) sprayers as the last public health protection strategy supported by the best available science (Marfin, et al., 2001).

The data collected and analyzed in this study were the results of large-scale mosquito and human surveillance and testing efforts throughout Ohio as part of the

71 implementation of the ODH’s mosquito control strategy (ODH, 2003). The availability of this historical database was indispensable to this research. Unlike previous Ohio studies, this descriptive study compiled and characterized data on human WNV disease onset, mosquito infection, bird density and infection, meteorological conditions, and mosquito control programs for the years 2002-2006 for the entire state and large metropolitan urban areas.

Five previous descriptive studies were found in the literature which characterized

WNV in Ohio mosquitoes, humans or bird hosts from 2001-2003. Two of these studies characterized the temporal and spatial patterns of WNV in mosquitoes (Mans et al., 2004;

White, Andrew, Mans, Ohajuruka, and Garvin, 2006), one study compared the human seroprevalence of WNV in three age categories (Mandalakas et al., 2005), one study evaluated exposure behaviors of infants and adults (LaBeaud, Kile, Kippes, King, and

Mandalakas, 2007), and one study characterized patterns of infection in Blue Jays

(Garvin, Tarvin, Smith, Ohajuruka, and Grimes et al., 2004).

From 2001-2002, Mans et al. (2004) completed a study in the City of Oberlin,

Ohio, which is located in Lorain County, Ohio. In the study, 12,151 mosquitoes representing 14 species were caught in gravid traps and 12,510 mostly Aedes vexans were captured in light traps in seven locations throughout the city. The mosquitoes were identified, sorted and tested in pools of no more than 50 mosquitoes using real-time reverse transcription polymerase chain reaction (RT-PCR). 75 of 82, or 92% of the positive pools were of the Culex pipiens/restuans complex. “Females of the following species also tested positive: Aedes vexans, Ochlerotatus canadensis, Oc. japonicus,

Orthopodomyia spp., and Oc. triseriatus” (Mans et al.), in addition to a single male

72 Anopheles punctipennis. The density of Culex pipiens/restuans peaked during the week of July 11th to July 18, 2002, and steadily declined until August 28, 2002. Culex pipiens/restuans minimum infection rates (MIRs) peaked during the week of August 8,

2002 (Mans et al.).

The second mosquito study by White et al. (2006) characterized the temporal and spatial patterns of WNV from June 19, 2003 to August 18, 2003 in Oberlin, Ohio, from seven sites. The researchers collected 12,055 mosquitoes from 17 species, and Culex pipiens/restuans accounted for 97.6% of all mosquitoes collected. Nine out of 10, or 90% of the positive mosquitoes were found to be Culex pipiens/restuans, and one Anopheles quadrimaculatus tested positive. In 2003, mosquito density peaked during the week of

July 7th, and MIR peaked during the week of August 18th.

Mandalakas et al. tested the blood of 96 randomly selected participants from

December 5-12, 2002, for WNV immunoglobulin G and M (IgG and IgM) recruited from

13 Cuyahoga County municipalities and nine Cleveland neighborhoods. Of the 1,209 participants tested, 34 were determined to be positive for WNV infection, or a countywide weighted seroprevalence rate of 1.9%, “which suggested that ≈1 West Nile neuroinvasive disease (WNND) case occurred for every 160 infected persons”

(Mandalakas et al.). Mandalakas et al. found that children were infected from 4.5-5 times higher than participants from 18-64 years old and ≥ 65 years old, but developed into a case by a factor of 110 times lower than the other age groups. LaBeaud et al. (2007) used the survey data collected from the same sample of 1209 study participants from

Mandalakas et al. and found that children spend much more time outdoors and are less

73 likely to use personal protection as compared to adults, which placed them at a higher risk for WNV disease. This behavior was affirmed by higher seroprevalence rates.

Garvin et al. (2004) examined the prevalence of WNV positive dead Blue Jays collected and tested in Ohio from May to August, 2002 and found that: 1) infection percentages increased from 3% in May to 90% in August; 2) there was no difference in infection percentages across the West, Central and East regions; and, 3) no difference was found in infection percentages among three age classes. This led the researchers to the conclusion that Blue Jays infected in 2001 were not likely to contribute to the initiation of the epizootic in 2002.

A sixth study by LaBeaud et al. (2008) was relevant to this research because it showed that a “higher risk of human infection was significantly associated with higher income areas, increased fractionation of habitat, and older housing.”

The primary research hypotheses explored were that increases in 2002 mosquito

IRs from the beginning of the mosquito season to week 34 will be associated with the following patterns: 1) increasing T during the GON, E_H, and OVPSD time-delayed indices, and the OVP index; and 2) decreasing CP during the E_H time-delayed index; 3) a decreased CP, increased T, and decreased PDI during the 2002 overwintering time- delayed index compared to 2003-2006; 4) a decreasing PDI during the POVPSD and

74 increasing 2002 mosquito IRs will be associated with an increase in WNV infection in humans within two to three weeks after the week of trapping; and mosquito IR increases will be preceded by and directly related to increases in mosquito abundance and bird deaths from WNV; and, 3) these data will also demonstrate a temporal and spatial progression of reported mosquito IRs and human WNV disease onsets, beginning first in southern metropolitan areas and temporally and spatially moving to northern metropolitan areas of Ohio.

This study attempted to increase the knowledge of WNV disease in Ohio by using descriptive statistical tools to characterize temporal and spatial patterns of: 1) T, CP, the

PDI, and DL related to changes in documented WNV mosquito infection; and, 2) reported human WNV disease, WNV positive bird deaths, mosquito IRs, and mosquito density (abundance), by week, year and Ohio County, and within the broader context of the U.S..

2.3 Methods

2.3.1 Study area

The study area included counties in Ohio that had mosquito surveillance programs, and human WNV disease reported to the ODH, with emphasis on the older urbanized metropolitan areas of Franklin County/Columbus City, Hamilton

County/Cincinnati, Lucas County/Toledo, Lorain County, and Cuyahoga

County/Cleveland (Appendix F). These areas had the highest number of WNV positive mosquito pools and/or the highest incidence of WNV human cases in the state during the

2002 and 2003 epizootic/epidemic.

75 2.3.2 Human WNV case onset data

Ohio human WNV probable and confirmed non-neuroinvasive and neuroinvasive disease data were accessed for the years 2002-2006 from the Ohio Disease Reporting

System (ODRS) by an epidemiologist at the ODH upon written request and approval from the ODH Institutional Review Board, by county name and onset date, and received electronically for this study. Probable and confirmed cases were determined using the

Centers for Disease Control and Prevention (CDC) case definition. Each human case onset date was converted to an onset week, and the total number of cases per week were aggregated by county and year.

2.3.3 Mosquito IR data

Mosquitoes were collected by local health departments, mosquito control districts, and local communities operating an adult mosquito surveillance program throughout

Ohio using gravid or CDC light traps according to standard baiting methods. Within each jurisdiction, trap locations were chosen randomly, or by using grid placement methods, foci of transmission based placement methods, or a combination of these methods, in safe locations with permission from property owners, if applicable. Jurisdictions maintained consistent trap locations for the entire mosquito season or changed locations depending on complaints or newly discovered foci of transmission based upon WNV infected mosquitoes or human disease data. Traps were placed overnight (i.e., trap night), and specimens were collected the next day, usually as soon as possible after sunrise.

Depending on the jurisdiction, the number of locations varied from one to over 30 each trap night. The number of trap nights per week varied among jurisdictions, with the one or two trap nights being the norm and three per week the exception, because of the

76 intensity of the labor necessary and subsequent costs incurred with each collection. Once collected, specimens from each trap were frozen, and treated as independent samples.

Specimens were then counted, and males and females were separated, Culex spp. were identified and placed in pools of 50 or fewer individuals, and the remaining species were pooled accordingly. Each pool was identified by trap number and shipped or driven to the

Vector-Borne Disease Program (VBDP) laboratory for RT-PCR testing for the WNV per standard methods of ribonucleic acid (RNA) extraction. Positive and negative pool results were recorded by lab technicians in an electronic spreadsheet. All positive results were given to the local jurisdiction by phone or e-mail message as soon as the test was completed. Each month, an updated arbovirus report in the form of the spreadsheet was sent by e-mail to mosquito control managers (ODH, 2005).

Mosquito infection data for the years 2002-2006 were accessed by direct written request to personnel in VBDP managing Ohio’s historical arbovirus database. The data were contained in a spreadsheet in which each row contained a unique pool identification number assigned by the lab technician who performed the RT-PCR test. The columns on the spreadsheet included data for the collection date and a trap identification number assigned by the surveillance agency, the species collected identified by VBDP lab personnel, the number of mosquitoes in each pool determined by the surveillance agency, the county of collection, the surveillance agency that submitted the specimens for testing,

RT-PCR results (+/- for WNV), date and year tested, all recorded by lab personnel.

77 stripped of all mosquito species except Culex spp. or Culex pipiens, to ensure an analysis that was not biased by the breeding or feeding behaviors of other mosquito species. IRs were calculated by two methods: 1) MIR, the number of positive mosquito pools in each sample (one sample per trap and one or more pools per sample) divided by the total number of mosquitoes tested times 1000; and, 2) maximum likelihood estimate (MLE) for each sample (one sample per trap and one or more pools per sample). The MLE is an estimate of the actual number of positive mosquitoes in a sample (using PooledInfRate software as an add-on to Excel, Biggerstaff, 2003). The sample MIR was used in place of the MLE only if every pool per sample was positive, which renders the differential equations used in the calculation of the MLE unsolvable.

After calculating the sample IRs, it was apparent that there were extreme outliers in the range of IRs for each sample, county, and week. These outliers were caused by very small sample sizes, such as one positive pool and a sample size of one, which calculated to an estimated MIR of 1000; one positive pool and a sample size of two calculated to an estimated MIR of 500, one positive pool and a sample size of four calculated to an estimated MIR of 250/1000, and two positive pools with a sample size of five calculated to an estimated MIR of 400/1000. Placing this phenomenon into perspective, for the year 2002, the state average MIR was 10/1000 for all counties. In some counties, the average MIR ranged from 30-50/1000 during peak transmission. To achieve an 80% probability of detection during peak transmission periods, sample sizes of 31-52 were found to be necessary (Gu and Novak, 2004). For the year 2002, 88 samples of size one to four mosquitoes out of 779 total pools (11%) were positive. In

2006, with a statewide MIR of 2/1000, only four samples of size one to four mosquitoes

78 out of 793 (0.5%) were positive. Because extremely high sample MIRs upwardly biased

weekly mean IRs, any estimates of MIRs greater than 200/1000 were removed for every

county, week, month, and year. Even though many estimates of MIRs were above the

upper fence, the box plots in Figure 2.1 indicated a distinct separation of points above an

MIR of 200/1000. After the removal of any significant outliers, IRs were sorted and

averaged by county, year, and week, and by week and year for further analysis.

Minimum Infection Rate Outliers for Selected Ohio Counties, 2002

1,000

800

600

400

Mimimum infection infection rate Mimimum

200 0

Butler County Cuyahoga County

Franklin County Hamilton County

Lake County Lorain County

Summit County Lucas County

Montgomery County Stark County

Figure 2.1. Minimum infection rate statistical outliers from selected Ohio counties, 2002

79 2.3.4 Live and dead bird surveillance data

Bird surveillance data were accessed by a written request to personnel overseeing the CDC’s ArboNet program (CDC, 2013) under the provisions of the “non-human arboviral surveillance data reported to ArboNET: data release guidelines.” Data on WNV positive birds were received electronically for years 2002-2006 by year and date of mortality (collection), bird species, and county of collection. These data were aggregated by county, week, and year, for further analysis. Local public health agencies and mosquito control districts collected dead birds upon a request/complaint from their constituents. Upon collection, birds were refrigerated or stored on ice or freeze packs immediately and held in this condition until shipment or delivery to the Ohio Department of Agriculture Animal Disease Diagnostic Laboratory (ADDL) for necropsy under the requirements of a Bio Safety Level 2 facility. Fresh kidneys were collected and submitted to the ODH Zoonoses/VBDP lab, where they were tested for WNV by virus-specific real- time RT-PCR. Selected cases were submitted to CDC for test confirmation, and local mosquito control managers were informed of positive results in the same manner that was used for positive mosquito results (ODH, 2005).

Live bird prevalence data of crows and Blue Jays were accessed for the years

2001-2006 by direct download from the National Audubon Society’s (NASs) Christmas

Bird Count (CBC) database by location of the bird count (NAS, 2013) and species. CBCs were performed during the holiday season every year. Bird counts were identified by name, sponsoring organization, latitude and longitude, and year. Each count listed the names of the participants, the number and name of each identified bird species, including common and taxonomic names. For each species, a ratio was given that compared the

80 number of birds counted (numerator) to the total number of hours dedicated to the count by participants, named the Num/Part ratio. Therefore, counts that trended upwardly or downwardly could have been biased by the number of hours dedicated to the count by participants during any year, which could have been affected by numerous factors such as the weather or the number of participants. Bird identifications were made or verified by knowledgeable birding experts within the count group. Bird count locations were mapped by latitude and longitude using Google Earth. Bird count data from each count or multiple counts were assigned to the county where the counts were located (Appendix A).

2.3.5 Meteorological data

Daily mean temperature (°C) and total daily precipitation (mm) data were downloaded from archived sources from the National Oceanic and Atmospheric

Administration (NOAA), National Climatic Data Center, and Land Based Data Sets from

20 primary weather stations located throughout Ohio (Appendix B) (NOAA, 2012), and converted to T or CP. Each county in Ohio was assigned to its closest weather station based upon distance and prevailing weather patterns in collaboration with a Columbus- based meteorologist (e-mail by Jym Ganahl, Chief Meteorologist, Channel 4, Columbus,

Ohio, January 25, 2013).

Daily day length data were retrieved from: http://www.timeanddate.com/worldclock/astronomy.html?month=8&year=2013&obj=su n&afl=-11&day=1&n=414, and converted to DL (minutes) from Canton for the eastern section of the state, from Columbus for the central portion of the state, and from

Cincinnati for the western portion (Appendix C). Palmer Drought Severity Index (PDI)

81 data were downloaded from NOAA U.S. climate monitoring weekly products (NOAA,

2012a) for each of the 10 Ohio weather districts (Appendix D).

Table 2.1 contains the estimated time-delays of the indices that were constructed as time periods relative to the week mosquitoes were trapped (weeks before and during the trapping week). Specifically, these time periods estimated the temporal position of phases of the mosquito life cycle, and the ecological conditions necessary for development within these phases, in relation to the trapping week. These indices were oviposition, the egg, larvae and pupae stage (i.e., eggs-to-hatch), the adult stage (which included the gonotrophic cycle and the extrinsic incubation period), two theorized time periods that were needed for optimum oviposition site development, and an overwintering time period from December to March. These indices were informed by T and CP data, the PDI, and DL (photoperiod). Multiple time periods were established for some indices for use in future statistical analysis to determine the indices that may be associated with mosquito and human WNV infections. A "-" symbol preceding the week number indicated a week prior to the trapping week (i.e., backwards in time) and a "+" symbol preceding the week number indicated the week of trapping that was the same week as the estimated OVP index. For example, to inform the E_H time-delayed indices

E_H, E_H1, E_H2 and E_H3 with T, T data from weeks -2 and -3 were added, and the sum was divided by 2, T for week -2 was used, T for week -3 was used, and mean T data from weeks -3 and -4 were added, and the sum was divided by 2, respectively. To inform the GON time-delayed indices GON, GON1, and GON2 with CP, CP data from weeks

+1 and -1 were summed, CP from week -1 was used, and CP data from weeks -1 and -2 were summed, respectively. The 7-day PDI was defined by NOAA as follows:

82 The PDI is calculated based on precipitation and temperature data, as well as the local Available Water Content (AWC) of the soil. From the inputs, all the basic terms of the water balance equation can be determined, including evapotranspiration, soil recharge, runoff, and moisture loss from the surface layer. Human impacts on the water balance, such as irrigation, are not considered. Complete descriptions of the equations can be found in the original study by Palmer (NOAA, 2012).

Index Time- Index code Index Description Delay OVP Oviposition Week +1 GON Gonotrophic Cycle Week +1 : -1 GON1 Gonotrophic Cycle1 Week -1 GON2 Gonotrophic Cycle2 Week -1 : -2 E_H Eggs-to-Hatch Week -2 : -3 E_H1 Eggs-to-Hatch1 Week -2 E_H2 Eggs-to-Hatch2 Week -3 E_H3 Eggs-to-Hatch3 Week -3 : -4 OVPSD Oviposition Site Development Week -5 : -7 OVPSD1 Oviposition Site Development1 Week -6 : -8 POVPSD Preoviposition Site Week -8 : -11 Development POVPSD1 Preoviposition Site Week -9 : -12 Development1 OW December to March December, (Overwinter) January, February Table 2.1. Estimated time-delays of the indices representing the mosquito life cycle and ecology which were constructed as time periods relative to the week mosquitoes were trapped (week +1 is the trapping week)

Figure 2.2 is a graphical illustration of the estimated time-delays of the indices relative to the week mosquitoes were trapped. The week of trapping is indicated by week

+1. The trap week (TW) was assumed to be the week of oviposition. Human exposure to

83 an infectious mosquito was assumed to have occurred during the GON time-delayed indices between weeks -2 to +1.

E-H2 E -H1 GON

POVPSD1 OVPSD1 E-H GON1

OW POVPSD OVPSD E-H3 GON2 OVP

-56 -52 -48 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 +1 Weeks before trap week (TW) TW

Oviposition (OVP) estimated time period Gonotrophic (GON) estimated time-delay

Eggs-to-hatch (E-H) estimated time-delay Oviposition site development (OVPSD) estimated time-delay Preoviposition site development (POVPSD) estimated time-delay

Overwinter (OW) estimated time-delay

Figure 2.2. A graphical representation of the estimated time-delays of the indices representing the mosquito life cycle and ecology including oviposition (OVP), gonotrophic (GON), eggs to hatch (E_H), oviposition site development (OVPSD, preoviposition site development (POVPSD) and overwinter (OW), which were constructed as time periods relative to the week mosquitoes were trapped (TW)

2.3.6 Mosquito control program data

During the spring and summer of 2013, a self-reporting survey and cover letter were mailed by the U.S. Postal Service to 87 Ohio local health department Environmental

Health Directors and one mosquito control district Director that had at least one human

WNV disease case from 2002-2006 to help document the components of their mosquito control programs. If a response was not received within three weeks, follow-up phone

84 calls or e-mails were used to determine if the survey was received and to expedite its return. In some cases, surveys were given over the phone or copies were e-mailed to the recipients to complete and return. Follow-up phones or e-mails were stopped when researchers received no response from their efforts. For local health department and mosquito control districts that indicated the use of adulticide or larvicide as a control measure, a self-reporting survey was mailed to document the weeks that these control measures were applied using a separate cover letter and survey form. Each question in the survey represented a potential component of an integrated mosquito control program.

2.4 Results

2.4.1 WNV in humans, 2002-2006

Table 2.2 combines the arbovirus activity in Ohio from 2002 to 2006 for human

WNV cases, and mosquitoes and birds that tested positive for WNV (CDC, 2013; ODH,

2010). Mosquitoes tested included all species and birds tested included live and dead birds.

Local health departments from 68 of 88 (77%) counties reported 669 human

WNV disease cases from 2002-2006. 87 of 127 (69%) Ohio city and county health departments had human cases within their jurisdictions. Ten of 68 (14.7%) counties had greater than or equal to ten cases from 2002-2006, with a range for all counties of one to

285. The median number of cases for all counties was 3.9. Appendix E contains a table of the Ohio counties with human cases from 2002-2006.

85 Total Human Neuro- Mosquitoes Pools Birds Birds Year Cases invasive Deaths Tested # Pools + Tested + 2001 0 0 0 91,590 No data 26 2890 286 2002 441 310 31 187,046 8,246 2082 5152 830 2003 107 84 8 490,249 19,796 799 2611 220 2004 12 11 2 398,832 14,202 874 3295 80 2005 61 46 2 390,010 14,705 1373 1817 73 2006 48 36 4 444,074 15,353 913 1589 125 Total 669 487 47 2,001,801 72,302 6,067 17,354 1,614 Table 2.2. Total Ohio human cases, neuroinvasive cases, deaths, number of mosquitoes tested, number of pools tested, number of pools positive for WNV, number of dead birds tested, and number of birds positive for WNV by year, 2002-2006

Table 2.3 compiles human case and WNV positive mosquitoes including all species and trapping methods in selected metropolitan urban counties containing the following cities (in parentheses): Cuyahoga (Cleveland), Franklin (Columbus), Hamilton

(Cincinnati), Lorain (Lorain), Lucas (Toledo), and Montgomery (Dayton). During the

2002 epidemic/epizootic, Cuyahoga (Cleveland) had 219 human cases, and Franklin

(Columbus), Hamilton (Cincinnati), Lorain (Lorain), Lucas (Toledo), and Montgomery

(Dayton) produced 9, 29, 15, 13, and 9 cases respectively, and collectively, produced

60% (401 of 669) of the total Ohio cases from 2002-2006 (ODH, 2010).

Figure 2.3 A illustrates the cumulative statewide epidemic curve from 2002-2006.

The peak of the epidemic occurred during onset weeks 35 and 36, with 98 and 97 cases respectively; and additional smaller peaks occurred at weeks 34, 37, and 38, with

86 Year 2002 2003 2004 2005 2006 Total Ohio County H M H M H M H M H M H M Cuyahoga 219 410 22 166 3 101 32 194 9 153 285 1024 Franklin 9 197 4 80 1 488 2 836 1 341 17 1942 Hamilton 29 396 4 160 1 123 5 86 1 48 40 813 Lorain 15 201 6 144 0 32 1 69 1 103 23 549 Lucas 13 15 3 16 0 31 4 44 5 12 25 118 Montgomery 9 17 2 27 0 5 0 7 0 4 11 60

Table 2.3. Human (H) WNV cases and WNV positive mosquito (M) pools for Cuyahoga,

Franklin, Hamilton, Lorain, Lucas, and Montgomery Counties in Ohio by year, 2002-

2006

A Cumulative Human WNV Cases by Onset Week, B Human Cases by Onset Week and Year, Ohio, 2002-2006 Ohio, 2002-2006 120 80 9897 70 100 90 85 86 60 2002 80 50 2003 60 40 2004 2005 30 40 Number of Cases Cases Number of 2006 number of Cases number of 20 20 10 0 0 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 24 26 28 30 32 34 36 38 40 42 44 46 Week Number Week Number C Human Cases by Onset Week, Cuyahoga County, Ohio, 2002-2006 45 40 35 2002 30 2003 25 2004 20 2005 15 2006 Number of Cases Number of 10 5 0 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 Week Number

Figure 2.3. Cumulative human cases by onset week for Ohio, 2002-2006 (A), human case onsets by week and year for Ohio, 2002-2006 (B), and human case onsets by week and year for Cuyahoga County, 2002-2006 (C) 87 85, 86, and 90 cases. Figure 2.3 B graphically separates each year showing an outlier peak at week 38 in 2003, and for the years 2002, 2004, 2005 and 2006, the peaks were in line with week 35. Figure 2.3 C illustrates a Cuyahoga County peak at week 34 in 2002 and 38 in 2003.

Figures 2.4 A to F illustrate peaks at weeks 34, 35, 35, 38, 36/37, and 39 for

Cuyahoga, Franklin, Hamilton, Lorain, Lucas, and Montgomery Counties, respectively.

Figure 2.5 A places this distribution of case onsets on the same graphic, while Figure 2.5

B expands the area between zero and nine cases to highlight those counties with lower numbers of overall cases.

A Cumulative Human Cases by Onset Week, B Cumulative Human Cases by Onset C Cumulative Human Cases by Onset Cuyahoga County, 2002-2006 Week, Franklin County, 2002-2006 Week, Hamilton County, 2002-2006 60 7 10 50 6 8 5 40 4 6 30 3 4

20 2 Number of Cases of Number Number of Cases Cases of Number 2 10 Cases of Number 1 0 0 0 31 32 33 34 35 36 37 38 39 40 41 42 43 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 31 32 33 34 35 36 37 38 39 40 41 Week Number Week Number Week Number

D Cumulative Human Cases by Onset E Cumulative Human Cases by Onset F Cumulative Human Cases by Onset Week, Lorain County, 2002-2006 Week, Lucas County, 2002-2006 Week, Montgomery County, 2002-2006 7 6 5 6 5 4 5 4 4 3 3 3 2 2 2

Number of CasesNumber Number of CasesNumber 1 Cases of Number 1 0 0 0 27 28 29 30 31 32 33 34 35 36 37 38 27 28 29 30 31 32 33 34 35 36 37 38 39 40 33 34 35 36 37 38 39 Week Number Week Number Week Number

Figure 2.4. Cumulative human cases by onset week for Cuyahoga (A), Franklin (B),

Hamilton (C), Lorain (D), Lucas (E), and Montgomery (F) Counties, 2002-2006

88 A Cumulative Human Cases by Onset Week for Select Ohio Counties, 2002-2006 50

Cuyahoga County 30 Franklin County Hamilton County Lorain County 20 Lucas County Number of Cases of Number Montgomery County

0 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 Week Number

B Cumulative Human Cases by Onset Week for Select Ohio Counties, 2002-2006 9 Cuyahoga County Franklin County Hamilton County Lorain County

Lucas County Number of Cases of Number Montgomery County 0 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 Week Number

Figure 2.5. Cumulative human cases by onset week for Cuyahoga, Franklin, Hamilton,

Lorain, Lucas, and Montgomery Counties, 2002-2006, with expanded view of the number of cases between 0 and 9 per week

2.4.2 WNV in mosquitoes and humans, 2002-2006

Mosquitoes were submitted from 75 of the 88 (81%) counties in Ohio from 2002-

2006 (no mosquitoes were submitted from 13 counties). Thirteen of the 75 (16%) counties with mosquitoes submitted for testing had no human WNV cases. Only negative mosquitoes were submitted from 17 counties out of 75 (58 counties had negative and positive mosquitoes), and 12 of the 17 counties had human cases reported (five had no human cases reported). Mosquitoes were not submitted from seven counties with human cases reported. Appendix F contains a table of the mosquito species tested for WNV, the

89 number of individual mosquitoes tested for each species, the number of mosquito pools tested per species, the number of positive pools, and the proportion of positive pools.

The proportion of Culex spp. to the total number of mosquitoes collected from 2002-2006 for Ohio ranged from 94%-96%. The proportion of positive mosquito pools of all species was 30% in 2002, 6% in 2003, 7% in 2004, 12% in 2005 and 8% in 2006. 55 different species of mosquitoes were collected and identified throughout the state.

Table 2.4 is a statewide summary of the number of pools tested, the number positive, the density, and the statewide aggregated MIR for only Culex spp. during the years 2002-2006, showing an approximate three to five-fold decrease in MIR from 2002 to 2003-2006. To justify the inclusion of Lake County’s light trap data, Table 2.5 shows that the percentage of Culex spp. caught in Lake County, even though mosquito control managers used a trap with bait (CO2) that attracted bridge vectors and mosquitoes attracted to mammalian hosts for blood meals, was similar when compared to gravid trap surveillance data.

# # Pos Year Pools pools Density MIR 2002 5669 1752 174652 10.0 2003 10840 724 361787 2.0 2004 10839 837 375304 2.2 2005 10769 1327 375799 3.5 2006 11569 896 420724 2.1 Total 49686 5536 1708266 3.2

Table 2.4. Number of Culex spp. pools tested, number of positive pools, number (density) of Culex spp. collected, and aggregated minimum infection rate (MIR) of Culex spp. for

Ohio by year, 2002-2006

Density all Density County species Culex % Culex

Cuyahoga 41083 40681 99 Franklin 8421 7206 86 Hamilton 23844 23100 97 Lake 12206 11666 96 Lorain 19456 19092 98 Lucas 953 806 85 Montgomery 3042 3037 100 Table 2.5. Cumulative density (count) of all species, density of Culex spp., and % Culex spp., for Cuyahoga, Franklin, Hamilton, Lake, Lorain, Lucas, and Montgomery Counties in Ohio, 2002-2006

Figures 2.6 to 2.12 illustrate the relationship of mosquito density, IR, human cases and adult control measures, and the resulting analyses are hypothetical and based only upon observation. The temporal distribution of density peaks were given more analytical strength than actual density, which was reduced by factors of 10-300 to allow for graphical representations relative to peaks in IR and human case onsets.

Figures 2.6 A and B show that in 2002, human cases peaked on the epidemic curve at week 36, but subtle shifts in the rate of change were noted between weeks 30-32,

32-34 and 34-36, when the rate of change showed a slightly arrested slope; on the downside of the epidemic curve, a minor peak occurred at week 38, the rate of change plateaued from weeks 39-40, and after a valley at week 41, peaked again at week 42. In

2003, human cases peaked on the epidemic curve at week 38, increased during weeks 28 and 29 and, after a decline at week 30, produced a smaller peak at week 31, and changed slope between weeks 34-35 and weeks 36-37.

91 A Infection Rate and Human Cases, Ohio, 2002

80 70 60 50 IRMLE 40 Human Cases 30 20

Number of human human cases of Number 10 0 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 IR: Number of infected mosquitoes 1000 per infected mosquitoes of Number IR: Week Number

B Infection Rate and Human Cases, Ohio, 2003

20 MLEIR 15 Human Cases

5 Number of human cases human of Number 0 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

IR: Number of infected mosquitoes per 1000 per mosquitoes infected of Number IR: Week Number

Figure 2.6. Average infection rate (IR) by trap week and total human cases by onset week for Ohio, 2002 (A) and 2003 (B)

A plateau appeared at weeks 40 and 41 after a consistent negative rate of change. The IR in 2002, Figure 2.6A, changed weekly and had its highest peak at week 34, smaller peaks at weeks 26 and 37, plateaued from weeks 28-29, and exhibited an arrested rate between weeks 30-33 and had valleys at weeks 33 and 36. In 2003, Figure 2.6 B, 2003 IR showed a small peak at week 34, the largest peaks at 37 and 39, and a valley occurred at week 38.

In 2002, it appeared that the peak IR precedes the peak of human case onsets by two weeks, i.e., the IR peak at week 34 was two weeks behind the human case onset peak at week 36. The IR peak in 2003 shows an association with the peak of human case onsets one week later than the IR peak. With this scenario, human exposure could be hypothesized to have taken place one week before trap week and during trap 92 week in Ohio during 2002 and 2003 respectively.

Figure 2.7 juxtaposed average weekly mosquito density (actual density divided by

10) with average weekly IR and weekly human case onsets for the years 2002 and 2003.

In 2002, Culex spp. density peaked at weeks 21, 23, 26, 33, plateaued at 35, and peaked again at weeks 38, compared to 2003 when peaks occurred at 18, 23, 26, 30, 33, 35, and

37, and after a valley at 40, peaked again at week 41. In 2002 on Figure 2.7, density

(actual density divided by 100) has a sub-peak one week prior to the IR peak at week 34, and IR reached an apex two weeks before the peak in human case onsets at week 36; and in 2003, density (actual density divided by 100) followed the same pattern with IR as

2002, but IR peaked only one week prior to the peak in human case onsets.

Density, Infection Rate, and Human Cases, Ohio, 2002-2003

120

100 Density(Density/10) 80 MLEIR 60 Human Cases

Number of human human cases of Number 20

3.4 2.2 2.3 2.4 3.2 3.3

2.36 2.37 3.41 2.19 2.21 2.22 2.23 2.24 2.25 2.26 2.27 2.28 2.29 2.31 2.32 2.33 2.34 2.35 2.38 2.39 2.41 2.42 3.17 3.18 3.19 3.21 3.22 3.23 3.24 3.25 3.26 3.27 3.28 3.29 3.31 3.32 3.33 3.34 3.35 3.36 3.37 3.38 3.39 3.42 IR: Number of infected mosquitoes 1000 per infected mosquitoes of Number IR:

Density: Number of mosquitoes mosquitoes week/10trap perNumber of Density: Year.Week Number

IR IR

Figure 2.7. Average mosquito density (actual average density divided by 10) and average infection rate (IR) by trap week, and total human cases by onset week for Ohio, 2002 and

2003, plus expanded view

93 Figure 2.8 A, Butler County, 2002, density (actual density divided by 100) at week 31 peaked an estimated two weeks prior to the IR peak at week 33, and IR appeared to peak the same week or one week prior to human case onsets. Figure 2.8 B, Cuyahoga

County, 2002, appeared to exhibit a similar trend of density (actual density divided

A Density, Infection Rate, and Human Cases, Butler County, 2002 60

40 Density(Density/100) MLEIR 30 Human Cases

2.26 2.23 2.24 2.25 2.27 2.28 2.29 2.30 2.31 2.32 2.33 2.34 2.35 2.36 2.37 Year.Week Number

B Density, Infection Rate, Human Cases, and Adulticiding, Cuyahoga County, 2002 70

50 Density(Density/100) IRMLE 40 Human Cases 30 Adulticide(1=yes, 0=no)

10 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1

2.32 2.23 2.24 2.25 2.26 2.27 2.28 2.29 2.30 2.31 2.33 2.34 2.35 2.36 2.37 2.38 2.39 Year.Week Number

Figure 2.8. Mosquito density (actual density divided by 100) and average infection rate

(IR) by trap week, adulticiding by control week, and total human cases by onset week for

Butler (A) and Cuyahoga (B) Counties, 2002

by100) peaks an estimated two weeks prior to IR peaks between weeks 30 and 32 and weeks 33 and 35, respectively, and two weeks between IR peaks and human case onset

94 peaks between weeks 32 and 34, 33 and 35, 34 and 36, 35 and the arrested rate of change between week 37 and 38, 36 and 38, and 37 and 39, respectively. Density decreased sharply possibly due to the effect of adult control methods (which were noted as a “1” or

“0” on the graph) or other ecological factors such as decreased day length causing the onset of diapause.

In Figure 2.9 A, Franklin County, the time between density (actual density divided by 100) and IR peaks was inconstant, with what appeared to be two weeks from weeks 31 (density)-33(IR) and weeks 33(density)-35(IR), and a one week difference from week 35 (density) to week 36 (IR). IRs appeared to peak between one and two weeks prior to the peaks in human case onsets. Mosquito control managers provided weekly adulticiding data, which is noted as a “1” or “0” on the graph and may have effected the density decreases seen at weeks 32 and 34. In Figure 2.9 B, Hamilton County, 2002, density (actual density divided by 100) fluctuations appeared to occur simultaneously with changes in IRs. IR peaked three weeks prior to the peak in human case onsets at week 35.

In Figure 2.10 A, Lake County, 2002, one-week lagged density (actual density divided by 100) increases may have been associated with increases in IRs during weeks

29-32 (density between weeks 28 and 30 probably reduced by adult control); and, after week 32, density became inversely related possibly due to the contribution of adult mosquito control beginning at week 24, or other ecological factors such as decreased day length causing the onset of diapause. There was a discernible trend between the increased

IRs from weeks 31-36 and human cases beginning in week 35.

95 A Density, Infection Rate, Human Cases, and Adulticiding, Franklin County, 2002

50 Density(Density/100) 40 MLEIR 30 Human Cases Adulticide(1=yes, 0=no) 20

10 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0

2.23 2.25 2.24 2.26 2.27 2.28 2.29 2.30 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 2.40 Year.Week Number

B Density, Infection Rate, and Human Cases, Hamilton County, 2002 60

50 Density(Density/100) 40 IRMLE 30 Human Cases 20

2.35 2.24 2.25 2.26 2.27 2.28 2.29 2.30 2.31 2.32 2.33 2.34 2.36 2.37 2.38 Year.Week Number

Figure 2.9. Mosquito density (actual density divided by 100) and average infection rate

(IR) by trap week, adulticiding by control week, and human cases by onset week for

Franklin (A) and Hamilton (B) Counties, 2002

In Figure 2.10 B, Lorain County, 2002, density (actual density divided by 100) appeared to increase directly and with a one-week lag behind IRs from weeks 27-32. Between weeks 32 and 35, density remained relatively low and IRs continued to decline until both hit bottom at week 35, after which IR exponentially increased to ~45 per 1000. Peak human case onsets occurred at week 35, three weeks after peak IR at week 32. Adult mosquito control appeared to reduce density beginning with week 31 along with possible ecological factors such as decreased day length causing the induction of diapause. 96

A Density, Infection Rate, Human Cases, and Adulticiding, Lake County, 2002 30

25 Density(Density/100) 20 MLEIR 15 Human Cases Adulticide(1=yes, 0=no) 10

5 0 1 1 1 0 1 1 1 0 0 1 1 1 1

2.30 2.23 2.24 2.25 2.26 2.27 2.28 2.29 2.31 2.32 2.33 2.34 2.35 2.36 Year.Week Number

B Density, Infection Rate, Human Cases, and Adulticiding, Lorain County, 2002 60

50 Density(Density/100) 40 IRMLE 30 Human Cases Adulticide(1=yes, 0=no) 20

10 0 1 1 0 0 0 1 1 1 1 1 1 1 1

2.29 2.24 2.25 2.26 2.27 2.28 2.30 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 Year.Week Number

Figure 2.10. Mosquito density (actual density divided by 100) and average infection rate

(IR) by trap week, adulticiding by control week, and human cases by onset week for Lake

(A) and Lorain (B) Counties, 2002

In 2003, Figure 2.11 A, Cuyahoga County, 2003, density (actual density divided by 200) may have been afected by adult mosquito control during weeks 29-38, or may have been influenced by other ecological factors. Density increased to three peaks at weeks 26, 30 and 34 followed by an IR peak at week 36 and a human case onset peaks at weeks 37 and 38. In Figure 2.11 B, Franklin County, 2003, density (actual density

97 divided by 100) and IR appeared to be directly related with a density peak two weeks prior to an IR peak from weeks 31-33, respectively. The decrease in density between

A Density, Infection Rate, Human Cases, and Adulticiding, Cuyahoga County, 2003 45 40 35 30 Density(Density/200) 25 IRMLE 20 Human Cases 15 Adulticide(1=yes, 0=no) 10 5 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 Week Number

Density, Infection Rate, Human Cases, and Adulticiding, Franklin County, 2003 B 35

30 Density(Density/100) 25 IRMLE 20 Human Cases Adulticide(1=yes, 0=no) 15

5 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 Week Number

Figure 2.11. Mosquito density (actual density divided by 200 for Cuyahoga County and actual density divided by 100 for Franklin County) and average infection rate (IR) by trap week, adulticiding by control week, and human cases by onset week for Cuyahoga (A) and Franklin (B) Counties, 2003

weeks 32 and 33 appears to be related to the valley in IR at week 34. The large increase in density at week 34 may be related to the increase in IR at week 35. There appears to be simultaneous and directly related fluctuations in density and IR between weeks 35 and

98 38. There were probably influences to density and IR by mosquito control for larvae and adults and ecological factors such as decreased day length causing the onset of diapause.

A trend was apparent between increased IRs and human case onsets between weeks 29 and 31, weeks 33 and 34, and week 37 and possibly the human case at week 41.

In Figure 2.12, Lorain County, 2003, the effect of adult control measures or other ecological factors such as decreased day length causing the onset of diapause may have contributed to the decreased density (actual density divided by 300) between weeks 33 and 40. Peak density appeared to precede peak IR by two to three weeks between weeks

27 and 29 and weeks 33 and 36, respectively. IR peaks appeared to precede peak human case onsets by one to two weeks.

Density, Infection Rate, Human Cases, and

Adulticiding, Lorain County, 2003 45

40 Density(Density/300) 35 30 IRMLE 25 20 Human Cases week/300 week/300 15 10 Adulticide(1=yes, Number of human human casesof Number 5 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 1 1 0 0=no) 0

Density: Number of mosquitoes per per trap mosquitoesof Number Density: 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 IR: Number of infected mosquitoes per per 1000 Number mosquitoes infected ofIR: Week Number

Figure 2.12. Mosquito density (actual density divided by 300) and average infection rate

(IR) by trap week, adulticiding by control week, and human cases by onset week for

Lorain County, 2003

99 Figures 2.13 A and B were constructed to investigate potential mosquito density trends in 2002, specifically if they followed spatial patterns of variation from southern to northern Ohio, or from urbanized versus suburbanized land use. Studies have shown an

A Density by Select Ohio Counties, 2002 70

60 Cuyahoga 50 Franklin Hamilton 40 Lake

week/100 Lorain 30 Mahoning Montgomery 20

Summit Density: Number of mosquitoes per trap per mosquitoes of Number Density: Butler 10

2.3

2.34 2.23 2.24 2.25 2.26 2.27 2.28 2.29 2.31 2.32 2.33 2.35 2.36 2.37 2.38 B Year.Week Number

Density by Select Ohio Counties, 2002 20 18 Cuyahoga 16 Franklin 14 Hamilton 12 Lake 10

week/100 Lorain 8 Mahoning 6 Montgomery 4 Summit Density: Number of mosquitoes per trap trap per mosquitoes of Number Density: 2 Butler

2.3

2.23 2.24 2.25 2.26 2.27 2.28 2.29 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 Year.Week Number

Figure 2.13. Mosquito density (actual density divided by 100) for Cuyahoga, Franklin,

Hamilton, Lake, Lorain, Mahoning, Montgomery, Summit, and Butler Counties in Ohio by trap week (A), and an expanded view of density between 0 and 2000 (B), 2002

association between urban land use WNV risk and mosquito density (Andreadis,

Anderson, Vossbrinck, and Main, 2004; Ruiz, Tedesco, McTighe, Austin, and Kitron,

2004). Hamilton and Cuyahoga Counties had mosquito density peaks at week 26,

100 30 and 31 and alternating peaks during weeks 32, 33, and 34 from Hamilton, Cuyahoga, and Hamilton, respectively, and have higher densities than the remaining counties graphed, with the exception of large peaks in density for Summit and Lake Counties at weeks 29 and 31, respectively. The remaining counties, at mosquito densities in the 1500 to 2000 per week range, seem to have followed a somewhat similar pattern of fluctuation, within a temporal shift of at most one week from Cuyahoga and Hamilton Counties.

2.4.3. WNV in birds

Figures 2.14 A to 2.14 G show the exponential decline in the CBC estimated crow population from 2001-2002, when the epizootic occurred in Ohio. The black (crows)

Figure 2.14. Audubon Society Christmas bird count (CBC) crow and Blue Jay density by year for Ohio (A), Cuyahoga County (B), Franklin County (C), Cincinnati and Hamilton

County (D) Lorain County (E), Lucus County (F), and Montgomery County (G) with blue trend lines for Blue Jays and black trend lines for crows, 2001-2006 101 and blue (blue jays) estimated Excel trend lines on Figures A to F are subject to errors in the count data from inconsistent Num/Part ratios between count years and the influence of roosting bird populations. Statewide, as seen in Figure 2.14 A, the CBC crow population rebounded in 2006 to above 2001 count levels. In individual counties, CBC crow repopulation varied, with the Franklin County population rebounding in 2005 and

2006, the Cuyahoga, Hamilton, Montgomery County populations never rebounding from

2002-2006, while the Lorain and possibly Lucas County populations experienced a moderate repopulation. The CBC Blue Jay population, statewide, recovered to meet or exceed pre-epizootic levels.

Table 2.6 is a summary of the number of WNV positive bird deaths by species and year and Appendix G contains a table that lists the 39 “other” bird species that tested positive, and the percent of each species (of the total number of other birds tested) that tested positive. 13% of the species other than crows and Blue Jays were robins, 10% were grackles, 22% were House Sparrows, and 13% were Northern Cardinals.

Species 2002 2003 2004 2005 2006 Total American Crow 319 39 18 25 30 431 Blue Jay 481 56 26 17 49 629 Other 30 125 36 31 46 268 Total 830 220 80 73 125 1328 Table 2.6. Number of WNV positive American Crows, Blue Jays, and other species by year, Ohio, 2002-2006

102 Figure 2.15 A shows the cumulative peak onset (collection) week for WNV dead birds in Ohio from 2002-2006. Figure 2.15 B is a graphic illustration of Table 2.6 and shows that, after crows and Blue Jays declined from 2002-2003, other birds became the host species to WNV. Figures 2.15 C and D exhibits the counties that contributed to the peaks in WNV positive birds seen on Figure 2.15 A at weeks 28, 31, and 32.

A Cumulative WNV Positive Birds, Ohio, B Arbovirus Host Species, 2002-2006 2002-2006 600 300 500 250 400 200 Crow 300 Blue Jay

150 Count

Count Other 100 200

50 100

0 0

29 20 21 22 23 24 25 26 27 28 30 31 32 33 34 35 36 37 38 42 19 2002 2003 2004 2005 2006 Week Number Year

C WNV Positive Birds, Select Ohio Counties, 2002 D WNV Positive Birds, Select Ohio Counties, 2002 20 30 18 16 Cuyahoga County 25 Allen County 14 Franklin County Green County 20 Licking County 12 Hamilton County Madison County

10 Lake County 15 Count Count Medina County 8 Lorain County Mercer County 6 10 Lucas County Ottawa County 4 Montgomery County 5 Portage County 2 Stark County Wood County 0 0 21 22 23 24 25 26 27 28 29 30 31 32 33 34 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Week Number Week Number

Figure 2.15. Cumulative number of WNV positive birds by week for Ohio, 2002-2006

(A), number of WNV positive birds by species and year for Ohio, 2002-2006 (B), number of WNV positive birds by week for Cuyahoga, Franklin, Hamilton, Lake, Lorain,

Lucas, Montgomery, and Stark Counties, 2002(C), and the number of WNV positive birds by week for Allen, Green Licking, Madison, Medina, Mercer, Ottawa, Portage, and

Wood Counties, 2002 (D)

103 2.4.4. WNV ecology

The indices presented in this section represent theoretical time periods of the mosquito’s development cycle and are informed with T, CP, the PDI, and DL, or photoperiod. These indices will be used in further statistical analysis to estimate the factors that most influence IR. Figures 2.16 A to 2.20 A present a longitudinal view of the changes in Ohio average index values from 2002-2006. For graphs with multiple indices informed with T or CP data, such as Figure 2.16 B and 2.16 C, the indices that may have an influential role in the variation of IR and mosquito density are those that exhibit the highest weekly peaks and the lowest weekly valleys depending on the hypothesis for each index.

Oviposition Index, Average Temperature by Week, Ohio, 2002-2006 A 30

20 TOVP

15 Temperature Temperature

10 2.27 2.29 2.31 2.33 2.35 2.37 2.39 3.24 3.26 3.28 3.3 3.32 3.34 3.36 3.38 3.4 4.25 4.27 4.29 4.31 4.33 4.35 5.25 5.27 5.29 5.31 5.33 5.35 5.37 5.39 6.27 6.29 6.31 6.33 6.35 6.37 6.39 Year.Week Number

Gonotrophic Indices, Average Temperature by Week, Ohio, 2002-2006 B 30

C 25

TGON 20 TGON1 TGON2

15 Temperature Temperature 10 2.27 2.29 2.31 2.33 2.35 2.37 2.39 3.24 3.26 3.28 3.3 3.32 3.34 3.36 3.38 3.4 4.25 4.27 4.29 4.31 4.33 4.35 5.25 5.27 5.29 5.31 5.33 5.35 5.37 5.39 6.27 6.29 6.31 6.33 6.35 6.37 6.39 Year.Week Number Eggs-to-Hatch Indices, Average Temperature, by Week, Ohio, 2002-2006

C 30 C

25 TE_H 20 TE_H1 TE_H2 TE_H3

Temperature Temperature 15

Figure 2.16. Oviposition (OVP) (A), gonotrophic (GON) (B), and eggs-to-hatch (E_H)

104 A Oviposition and Preoviposition Site Development Indices, Average Temperature by Week, Ohio, 2002-2006 30

25 C TOVPSD 20 TOVPSD1 15 TPOVPSD 10 TPOVPSD1

Temperature Temperature 5

3.3 3.4

2.27 2.29 2.31 2.33 2.35 2.37 2.39 3.24 3.26 3.28 3.32 3.34 3.36 3.38 4.25 4.27 4.29 4.31 4.33 4.35 5.25 5.27 5.29 5.31 5.33 5.35 5.37 5.39 6.27 6.29 6.31 6.33 6.35 6.37 6.39 Year.Week Number

B Overwinter Index, Average Temperature by Week, Ohio, 2002-2006 4

C 3 2 1 0 TOW -1 2.27 2.29 2.31 2.33 2.35 2.37 2.39 3.24 3.26 3.28 3.3 3.32 3.34 3.36 3.38 3.4 4.25 4.27 4.29 4.31 4.33 4.35 5.25 5.27 5.29 5.31 5.33 5.35 5.37 5.39 6.27 6.29 6.31 6.33 6.35 6.37 6.39

Temperature Temperature -2 -3 -4 -5 Year.Week Number

C Oviposition Index, Average Cumulative Precipitation by Week, Ohio, 2002-2006 100 80 60 CPOVP 40 20

3.3 3.4

Cumulative Precipitation mm Precipitation Cumulative

2.35 2.27 2.29 2.31 2.33 2.37 2.39 3.24 3.26 3.28 3.32 3.34 3.36 3.38 4.25 4.27 4.29 4.31 4.33 4.35 5.25 5.27 5.29 5.31 5.33 5.35 5.37 5.39 6.27 6.29 6.31 6.33 6.35 6.37 6.39 Year.Week Number

Figure 2.17. Oviposition (OVPSD) and preoviposition (POVPSD) site development (A) and overwinter (OW) (B) time-delayed indices informed with weekly mean temperature

(T); oviposition (OVP) (C) index informed with weekly mean cumulative precipitation

(CP), Ohio, 2002-2006

A Gonotrophic Indices, Average Cumulative Precipitation by Week, Ohio, 2002-2006 160 140 120 100 CPGON 80 CPGON1 60 CPGON2 40 20

Cumulative Precipitation mm Precipitation Cumulative 0 2.27 2.29 2.31 2.33 2.35 2.37 2.39 3.24 3.26 3.28 3.3 3.32 3.34 3.36 3.38 3.4 4.25 4.27 4.29 4.31 4.33 4.35 5.25 5.27 5.29 5.31 5.33 5.35 5.37 5.39 6.27 6.29 6.31 6.33 6.35 6.37 6.39 Year.Week Number B Eggs-to-Hatch Indices, Average Cumulative Precipitation by Week, Ohio, 2002-2006 140 120 100 CPE_H 80 CPE_H1 60 CPE_H2 40 CPE_H3 20

Cumulative Precipitation mm Precipitation Cumulative

3.3 3.4

2.39 2.27 2.29 2.31 2.33 2.35 2.37 3.24 3.26 3.28 3.32 3.34 3.36 3.38 4.25 4.27 4.29 4.31 4.33 4.35 5.25 5.27 5.29 5.31 5.33 5.35 5.37 5.39 6.27 6.29 6.31 6.33 6.35 6.37 6.39 Year.Week Number C Oviposition and Preoviposition Site Development Indices, Average Cumulative Precipitation by Week, Ohio, 2002-2006 250 200 CPOVPSD 150 CPOVPSD1 100 CPPOVPSD 50 CPPOVPSD1 0

2.27 2.29 2.31 2.33 2.35 2.37 2.39 3.24 3.26 3.28 3.3 3.32 3.34 3.36 3.38 3.4 4.25 4.27 4.29 4.31 4.33 4.35 5.25 5.27 5.29 5.31 5.33 5.35 5.37 5.39 6.27 6.29 6.31 6.33 6.35 6.37 6.39 Cumulative Precipitation mm Precipitation Cumulative Year.Week Number

Figure 2.18. Gonotrophic (GON) (A), eggs-to-hatch (E_H) (B), oviposition (OVPSD) and preoviposition (POVPSD) site development time-delayed indices (C) informed with weekly mean cumulative precipitation (CP), Ohio, 2002-2006 105 A Overwinter Index, Average Cumulative Precipitation by Week, Ohio, 2002-2006 350 300 250 CPOW 200 150 100 2.27 2.29 2.31 2.33 2.35 2.37 2.39 3.24 3.26 3.28 3.3 3.32 3.34 3.36 3.38 3.4 4.25 4.27 4.29 4.31 4.33 4.35 5.25 5.27 5.29 5.31 5.33 5.35 5.37 5.39 6.27 6.29 6.31 6.33 6.35 6.37 6.39 Cumulative Precipitation mm Precipitation Cumulative Year.Week Number B Oviposition and Preoviposition Site Development Indices, Average Palmer Drought Index by Week, Ohio, 2002-2006 6.00

4.00 PDIOVPSD

2.00 PDIOVPSD1 PDIPOVPSD Index 0.00 PDIPOVPSD1 2.27 2.29 2.31 2.33 2.35 2.37 2.39 3.24 3.26 3.28 3.3 3.32 3.34 3.36 3.38 3.4 4.25 4.27 4.29 4.31 4.33 4.35 5.25 5.27 5.29 5.31 5.33 5.35 5.37 5.39 6.27 6.29 6.31 6.33 6.35 6.37 6.39 -2.00

-4.00 Year.Week Number C Over Winter Index, Average Weekly Palmer Drought Index by Week, Ohio, 2002- 2006 7.00 6.00 5.00 4.00 PDIOW

3.00 Index 2.00 1.00 0.00 2.27 2.29 2.31 2.33 2.35 2.37 2.39 3.24 3.26 3.28 3.3 3.32 3.34 3.36 3.38 3.4 4.25 4.27 4.29 4.31 4.33 4.35 5.25 5.27 5.29 5.31 5.33 5.35 5.37 5.39 6.27 6.29 6.31 6.33 6.35 6.37 6.39 -1.00 Year.Week Number

Figure 2.19. Overwinter (OW) time-delayed index (A) informed with weekly mean cumulative precipitation (CP); oviposition (OVPSD) and preoviposition (POVPSD) site development (B) and OW (C) time-delayed indices informed with weekly Palmer

Drought Index (PDI), Ohio, 2002-2006

The hypotheses were tested using basic descriptive analysis, and the time-delayed indices that could have been important to changes in IR during the epidemic year 2002 were noted, given the evidence that: 1) temperature and drought conditions may have a direct relationship with density and MIR and precipitation may have an inverse relationship with density (Epstein, 2004; Gong, DeGaetano, and Harrington, 2011;

Hartley et al., 2012; Irwin, Arcari, Hausbeck, and Paskewitz, 2008; Kuhn, Campbell-

Lendrum, Haines, Cox, 2004; Kunkel, Novak, Lampman, and Gu, 2006; Ladeau, Marra,

Kilpatrick, and Calder, 2008; Morin and Comrie, 2010; Ruiz et al., 2010; Shaman, Day, and Stieglitz, 2005); 2) DL (photoperiod) may have a direct effect on density during the gonotrophic cycle and oviposition (Gong, Degaetano, and Harrington, 2007; 106 Vinogradova, 2000) by inducing diapauses; and, 3) changes in MLE/MIR and density may influence human case onset weeks (Liu et al., 2009).

Figures 2.20 C to 2.22 E display Ohio weekly indices informed with T, CP, PDI, and DL for the year 2002. Figure 2.20 A depicts the days becoming shorter between weeks during the OVP and GON time-delayed indices from 2002-2006. Figure 2.20 B exhibits the actual DL divided by 10 by week from the longest week to the shortest week of the year. Figure 2.20 B also illustrates the rate of change between weeks which was calculated as the difference between the day lengths of two consecutive weeks. The average weekly rate of change during the DLOVP, DLGON, and DLGON1 time-delayed indices decreased to less than one minute after week 33 and further declined until between 37 and 42, during the critical diapause inducing DL range for Culex spp., when the rate of change approached zero (Vinogradova, 2000) (Figure 2.10 C). In comparison, the average weekly rate of change for the DLGON2 time-delayed index was more than one minute, or a slower rate, which may have contributed to the peak in IRs at week 34 possibly due to the slightly longer day length and high mosquito densities. In Figure 2.20

D, decreasing drought indices (higher drought conditions) during the PDIOVPSD and

PDIOVPSD1 and PDIPOVPSD and PDIPOVPSD1 time-delayed indices between weeks

28 and peak IR at 34 (see red trend lines) may have contributed to the increased IR and increasing PDIOVPSD and PDIOVPSD1 indices (lesser drought conditions) may have influenced the decline in IR from week 34 to the end of the season (see red trend lines).

107 A Oviposition and Gonotrophic Indices, Average Day B Day Length Rate of Change by Week, Length by Week, Ohio, 2002-2006 Cleveland, Ohio, 2002 920 20

19 19 19 19 19 19 18 18 18 17 18 15 16 17 16 860 15 DLOVP 14 14 Rate of change 12 12 DLGON 10 between weeks

800 DLGON1 9 9

Minutes Minutes Minutes DLGON2 5 7 7 Day Length = Actual Day 740 4 3.29 Length/100 0

2.43 2.25 2.27 2.29 2.31 2.33 2.35 2.37 2.39 2.41 2.45 2.47 2.49 2.51 -2 680 -5

3.3 Year.Week Number

3.26 5.25 2.27 2.31 2.35 2.39 3.34 3.38 4.25 4.29 4.33 5.29 5.33 5.37 6.27 6.31 6.35 6.39 Year.Week Number

C D Oviposition and Gonotrophic Indices, Average Change in Day Over Winter, Oviposition and Preoviposition Site Length by Week, Ohio 2002 Development Indices, Average Palmer Drought Index by 18 DLOVP Week, Ohio, 2002 17 16 DLGON 2 15 1.5 14 DLGON1 13 1 PDIOVPSD 12 DLGON2 11 0.5 PDIOVPSD1 10 9 0 8 PDIPOVPSD -0.5 2.27 2.28 2.29 2.3 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 7 PDIPOVPSD1 6 Index 5 -1 4 PDIOW 3 -1.5 2

Minutes Between WeeksBetween Minutes -2 1 0 -2.5 -1 2.27 2.28 2.29 2.3 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 -3 Year.Week Number Year.Week Number

Figure 2.20. Oviposition (OVP) index and gonotrophic (GON) time-delay index informed with weekly mean day length (DL), Ohio 2002-2006 (A); actual DL divided by 100 by week from the longest week to the shortest week of the year, Ohio 2002 (B); DL rate of change between weeks, Cleveland, Ohio, 2002 (B); expanded view of DL rate of change between weeks from 15 and 18 minutes between weeks, Cleveland, Ohio, 2002 (C); overwinter (OW), oviposition (OVPSD) and preoviposition (POVPSD) site development time-delayed indices informed with weekly Palmer Drought Index (PDI), Ohio, 2002 (D)

When comparing Figures 2.6 and 2.7 to Figures 2.21 and 2.22 during 2002,

Figure 2.21 A (see red trend line) revealed a weak trend of an increasing T during the

TGON2 time-delayed index that may have been relevant to the increasing IRs between weeks 28 and 34, but the extreme variation presented during weeks 28, 29, 32, and 33 108 would warrant a cautious interpretation. In Figure 2.21 B, the TE_H3 time-delayed index

A Oviposition and Gonotrophic Indices, Average Temperature B Eggs-to-Hatch Indices, Average Temperature by Week, Ohio, 2002 by Week, Ohio, 2002 28 28

27 27 C

C 26 26

TE_H TOVP 25 25 TE_H1 TGON 24 24 TE_H2 TGON1 23 TE_H3

23 TGON2 Temperature Temperature Temperature Temperature 22 22 21 21 20 20 2.27 2.28 2.29 2.3 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 2.27 2.28 2.29 2.3 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 Year.Week Number Year.Week Number

C Overwinter, Oviposition and Preoviposition Site D Oviposition and Gonotrophic Indices, Average Development Indices, Average Temperature by Week, Ohio, 2002 Cumulative Precipitation by Week, Ohio, 2002 80 30 24 24 25 24 24 24 23 24 23 70 25

20 21 TOVPSD 60 C 17 25 25 CPOVP 20 24 24 23 24 24 TOVPSD1 50 16 22 CPGON 20 21 TPOVPSD 15 40 CPGON1 17 TPOVPSD1 14 15 30 CPGON2 10 TOW

Temperature Temperature 20 5 10

0 mm Precipitation Cumulative 0 2.27 2.28 2.29 2.3 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 2.27 2.28 2.29 2.3 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 Year.Week Number Year.Week Number

Figure 2.21. Oviposition (OVP) and gonotrophic (GON) (A), eggs-to-hatch (E_H) (B), overwinter (OW), oviposition (OVPSD) and preoviposition (POVPSD) site development time-delayed indices (C) informed with weekly mean temperature (T), Ohio, 2002; oviposition index and gonotrophic time-delayed index informed with weekly mean cumulative precipitation (CP), Ohio, 2002 (D)

exhibited a strong trend that may reflect an association between increasing T and IRs beginning at week 30, to peak IRs at week 34 (see red trend line), coupled with a decrease in the index and IRs until the end of the season. In Figure 2.21 C, there may have been an association between the increase in T during the OVPSD and OVPSD1 and

POVPSD and POVPSD1 time-delayed indices and the increase in IRs until week 34 (see

109 red trend lines), but that does not explain the disassociation between a constant ~25°C T for these indices and decreased IRs from week 35 to the end of the season. In Figure 2.21

D, the CP from week 28 to week 34 during the OVP and GON time-delayed indices do not appear to be associated with an increase in IRs within that time frame.

In Figure 2.22 A, the CP from week 28 to week 34 during the E_H time-delayed indices do not appear to be associated with an increase in IRs within that time frame. In

A Eggs-to-Hatch Indices, Average Cumulative C Over Winter Index, Average Weekly Palmer Precipitation by Week, Ohio, 2002 Drought Index by Year, Ohio, 2002-2006 60 6 50 5 CPE_H 40 CPE_H1 4 PDIOW

30 CPE_H2 3 Index 20 CPE_H3 2 10 1 0 Cumulative Precipitation mm Precipitation Cumulative 2.27 2.28 2.29 2.3 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 0 2002 2003 2004 2005 2006 Year.Week Number Year D Over Winter Index, Average Weekly Cumulative Precipitation by Year, Ohio, 2002-06 300 250 200 CPOW 150 B 100 50 Overwinter, Oviposition and Preoviposition Site

Cumulative Precipiation mm Precipiation Cumulative 0 Development Indices, Cumulative Precipitation by Week, 2002 2003 2004 2005 2006 Ohio, 2002 Year 200 180 E 160 Over Winter Index, Average Weekly CPOVPSD 140 CPOVPSD1 Temperature by Year, Ohio, 2002-2006 120 3 CPPOVPSD

100 2 C

CPPOVPSD1 80 1 60 CPOW 0 40 TOW -1 2002 2003 2004 2005 2006 20 Cumulative Precipitation mm Precipitation Cumulative 0 -2 2.27 2.28 2.29 2.3 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 Temperature -3 Year.Week Number -4 Year

110 Figure 2.22 B, decreasing CP during the OVPSD and OVPSD1 and POVPSD and

POVPSD1 time-delayed indices between weeks 28 and peak IR at 34 (see red trend lines) may have contributed to the increased IRs, and increasing CPOVPSD and CPOVPSD1 indices may have influenced the decline in IR from week 34 to the end of the season (see

red trend line), as postulated by Gubler, Reiter, Ebi, Yap, Nasci, and Patz (2001a) and

Hunter (2003). A higher CP during the POVPSD and POVPSD1 compared to OVPSD and OVPSD1 time-delayed indices between weeks 28 and 34 may have contributed to the increased IRs because (even though both indices are decreasing) there needs to be some rainfall to provide the water for egg-laying and then relatively little rainfall to provide the stagnant water conditions. Comparing Figures 2.22 C, 2.22 D, and 2.22 E, the high overwinter T time-delayed index (see red trend line) may have had the most influence over the density of mosquitoes that entered the 2002 season as adults.

2.4.5. Mosquito control program data

Table 2.7 summarizes the results of a survey of Ohio local health departments and one mosquito control district that enumerated the percentage of the total mosquito control programs surveyed that received an index value of between 0 and 13. 83% of the surveys were returned after all follow-up attempts were exhausted, and results were recorded in an Excel spreadsheet. A higher index score represented the inclusion of more program components, which were numbered as questions on the survey from a lower to a higher degree of program quality, complexity, and cost. From 2002-2006, approximately 25% of the programs surveyed had an index of 3, which translated to providing public education and following up on complaints. 13% of the programs received an index of 5, which

111 typically suggested that programs included an adult surveillance component, but not adulticiding. About 14% of the program managers indicated that they included an adult mosquito control component such as truck-based spraying and received an index of

8. Five % of the programs received an index of 12, and included adulticiding based exclusively on surveillance data, a larviciding component, and honored “no spray requests.” Approximately 1.5% of the programs reached the index value of 13, and integrated post-control surveillance which verified a reduction in mosquito density.

Year 2002 2003 2004 2005 2006 Program Index % of programs with index 0 3 3 3 3 3 1 6 4 6 6 7 2 11 8 10 10 11 3 24 28 24 24 24 4 8 10 10 10 11 5 13 13 14 14 11 6 8 6 4 4 3 7 4 6 7 7 7 8 14 15 13 13 13 9 1 0 1 1 0 10 3 1 1 1 1 11 1 1 1 1 1 12 3 4 6 6 6 13 1 1 1 1 3 Table 2.7. Percentage of Ohio mosquito control programs

with index values between 0 and 13 by year

2.4.6 Characterization of the temporal relationship of mosquito density,

WNV positive bird deaths, mosquito IR, and human case onset dates

Figure 2.23 integrates mosquito density, WNV positive bird deaths, mosquito IR, and human case onset dates on one graphical presentation for comparison.

112 Actual mosquito density was reduced by a factor of ten and actual WNV positive bird numbers were reduced by a factor of two to be able to show the relative heights of peaks on one graph. In 2002, the human case onsets epidemic peak was preceded one to two weeks by IR peaks, three to four weeks by peaks in bird deaths, and five to six weeks by peak mosquito density. Increasing bird deaths (except for the anomaly between weeks 29 and 31) and increasing mosquito density appeared, with a one to two-week lag, to trend with increasing IRs.

Density, WNV + Birds, IR, Human Cases, Ohio, 2002

120 100

Density(Density/10) 80 WNV + Birds(WNV + 60 Birds/2)

40 Infection Rate Weekly Value Weekly 20

Human Cases

19 21 23 25 27 29 31 33 35 37 39 41 Week Number

2.5 Discussion

This study was an exploratory descriptive analysis to gain insight into the potential influences on increased mosquito IRs and human WNV case onsets. The 113 underlying biological and ecological factors that may have been driving the IR and mosquito density shifts were transformed into time-delayed indices representing theoretical phases of the mosquito life cycle and ecology relative to the week mosquitoes were collected, and these indices were informed with T, CP, the PDI, and DL data, in order to better understand the relative importance of each index in explaining the variation of IR and ultimately the onset of human WNV cases. These time-delayed indices based upon this writer’s experiences at mosquito control and the results of studies found in the literature, which may have had an affect on the indices that not only were developed, but chosen in this study as potentially influential.

The primary research hypotheses explored were that increases in 2002 mosquito

OVPSD time-delayed indices; 5) an increased amount of CP during the POVPSD time- delayed index compared to a decreased amount during the OVPSD time-delayed index; and, 6) decreasing DL during the OVP index and the GON time-delayed index. Three additional hypotheses were explored: 1) decreasing 2002 mosquito density (abundance) after week 34 will be associated with a decrease in DL; 2) increasing 2002 mosquito IRs will be associated with an increase in WNV infection in humans within two to three weeks after the week of trapping; and mosquito IR increases will be preceded by and directly related to increases in mosquito abundance and bird deaths from WNV; and, 3)

114 these data will also demonstrate a temporal and spatial progression of reported mosquito

IRs and human WNV disease onsets, beginning first in southern metropolitan areas and temporally and spatially moving to northern metropolitan areas of Ohio.

It was apparent from the analysis of the descriptive statistics presented, that temporal and spatial patterns of temperature, precipitation, and the PDI were discovered that were possibly related to increases in 2002 mosquito IRs from the beginning of the season to week 34. Figure 2.24 graphically illustrates with red arrows the patterns that were found: 1) T increases from 24°C-25°C during the GON2 time-delayed index, from

21°C-26°C during the E_H3 time-delayed index, and an average of 2°C overwinter

(December, January, February); 2) PDI decreases from -1.25 to -2.5 during OVPSD and

OVPSD1 and from + 0.5 to -2.0 during the POVPSD and POVPSD1 time-delayed indices; 3) T increases from 15°C-24°C during OVPSD and OVPSD1 and from 15°C to

~21°C during the POVPSD and POVPSD1 time-delayed indices; and, 4) CP ~ four times higher during POVPSD and POVPSD1 compared to the OVPSD and OVPSD1 time-delayed indices.

The hypotheses that were not supported by the descriptive analysis were that increases in 2002 mosquito IRs from the beginning of the season to week 34 will be associated with the following patterns: 1) increasing in T during OVP index; 2) decreasing CP during the E_H time-delayed index; 3) decreased CP and PDI during the

2002 overwintering time-delayed indices when compared to 2003-2006; and, 4) the decreasing DL pattern during the OVP index and the GON time delayed index that was

“associated” with increasing IRs was a natural phenomenom and this hypothesis should not have been included in the analysis.

115 PDI T CP PDI T C P

T PDI T CP PDI T CP T T

-56 -52 -48 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -T1 +1 Weeks before trapping week (TW) TW

Figure 2.24. Time-delayed indices informed with weekly mean temperature (T), weekly cumulative precipitation (CP), and the Palmer Drought Index (PDI) estimated from the descriptive study to be associated with increases in IRs

An unexpected finding was the possible relationship between increasing IRs and increasing T during the POVPSD and POVPSD1 time-delayed indices.

The first of the three additional hypotheses questioned the temporal relationship of DL and mosquito density. It was apparent in all of the counties analyzed that mosquito density decreased from its peak to the end of the mosquito season. What was not known was the relative influence of control measures compared to natural density declines due to the onset of diapause caused by decreasing day length.

The second of the additional hypotheses, that an increasing 2002 mosquito IRs will be associated with an increase in WNV infection in humans within two to three weeks after the week of trapping, was supported because the data analyzed in this study showed lags of one to two weeks between peak mosquito IRs and peak human case onsets statewide in 2003 and 2002 in Figure 2.7, respectively, and between one to three weeks when individual counties were analyzed.

When compared to a broader context of literature within Ohio and other areas in the U.S., the conclusions reached in this study were comparable. The Ohio peak of 116 human case onsets for the epidemic year 2002 at week 35 was aligned with the United

States (U.S.) peak during 1999-2008. Ohio mosquito IR and density during 2002 and

2003 exhibited similar trends when compared to surveillance results in the Northeast U.S. during the beginning of the U.S. epidemic in 1999-2000. Bernard et al. (2001) noted a peak Culex pipiens IRs at weeks 33 and 34 from mosquitoes collected in New York State in 2000, similar to what was found in Ohio during 2002 from this study, and peaks in crow deaths at week 36, compared to week 32 in Ohio. Marfin et al. (2001) studied WNV in Pennsylvania, New York, Maine, New Jersey and Connecticut in 2000 and reported peak numbers of positive mosquitoes at week 34, peak bird deaths at week 33, and peak human cases at week 34 (which was contradicted by this study), which found peak bird deaths at week 32 and peak human cases at weeks 35 and 36. Mans et al. (2004) study of

WNV in mosquitoes from Oberlin in Lorain County, Ohio, in 2002, documented a peak mosquito IR at week 32, similar to the results found from this study in Figure 2.10 B within a temporal shift of one week, and mosquito density peaks at weeks 30, 32 and 34, which were similar to the peaks found in this study within one week. A comparable Ohio study conducted in Oberlin in 2003 by White et al. (2006), found mosquito density peaked at weeks 28 and 31 and IR peaked at week 34, whereas in this study, peaks were found at weeks 27, 33, and 36 respectively. The differences between results from the previous studies and this research could be attributed to the aggregation of statewide data compared to the analysis of data from one localized community, localized transmission dynamics between birds and mosquitoes, and localized patterns of weather that may have affected changes in mosquito density and IR.

117 Soverow, Wellenius, Fisman, and Mittleman (2009) performed an ecological study of 18 states scattered across the U.S. from Pennsylvania to California, including

Ohio, to estimate if precipitation and temperature had an effect on human WNV disease onsets. The study found that peaks in CP and 8-14 days prior to human case onsets T produced a significantly higher risk, assuming a 14 day case onset period. However, the

Soverow et al. (2009) study did not take into account the time needed for oviposition, which was limited to one week in this study (the OVP index during the trap week).

Except for not accounting for the OVP time, the peak T and CP time frames that were assumed to be within Extrinsic Incubation Period (EIP) time frame in the Soverow et al. study coincided with the GON, GON1 and GON2 time-delayed indices estimated in this research. These indices were the theoretical time periods prior to the trap week needed for blood meals, egg development, and virus incubation and amplification.

The temporal pattern of peak WNV positive bird deaths, IR, and human case onsets in Figure 2.23 was similar to a pattern that was observed during the New York

City outbreak in a Staten Island study in 2000: “an elevated Culex pipiens MIR and increased dead bird densities were present two weeks before the first human illness arrived” (Kulasekera et al., 2001). Andreadis et al. (2004) and Liu et al. (2009) documented high mosquito IRs two to four weeks before human cases and that risk factors for WNV disease were the presence of positive birds and positive mosquitoes 30 days before human cases appeared.

It was evident from the data presented that there could have been underlying biological, ecological and anthropogenic factors that may have been driving density and

IR peaks in 2002 and 2003 that may have influenced peaks in human case onsets. 118 Statewide in Figure 2.7, it appeared that density increases lagged behind increases in IRs by four weeks and were directly related. This time-lag does not hold in every county analyzed, where density peaks appeared to lag behind and were directly related to IR peaks by one to two weeks or fluctuated simultaneously.

An additional pattern that was recognized within the same sampling week was that when density was low, IRs were generally inversely related and higher. Since calculations of IRs are density driven, sampling errors affecting density (weather conditions, mosquito parous/gravid cycles, brew mixtures) may render the estimation of

IRs an artifact of sampling as concluded by Katholi and Unnasch (2006) or the calculation method (with density as the demominator).

It was less obvious and more difficult to ascertain the true relationship between density, WNV positive bird deaths, and IR peaks: 1) were density increases driving IR increases; 2) was there a discernable and biologically relevant lag between mosquito density peaks, corvid bird abundance due to bird deaths from WNV, and IR peaks; and,

3) did the relationship between corvid bird deaths and density contribute to the peaks in mosquito IRs. Wonham and Lewis (2008) reported that decreasing mosquito abundance will decrease mosquito IRs, but decreasing bird hosts from the WNV transmission cycle will cause fewer highly virulent bird hosts and will result in an increasing mosquito IR.

As shown in Figure 2.23 statewide, IRs and the number of WNV positive dead birds seemed to be indirectly related, supporting the theory proposed in Wonham and Lewis

(2008). It seemed that there may have been a statewide and local trend in 2002 of less time between the temporal lags of density, IRs, and human case onsets during week 30 to week 38, and a longer time between weeks 23 to 30 and 38 to the end of the season, i.e.,

119 these trends vary with time and temperature. There may have been a biological reason for the lag time between density increases (peaks and subpeaks) and variations in IRs. As reported by Anderson, Main, Delroux, and Fikrig (2008) and others, this pattern could have been explained by the temperature dependent and time variable extrinsic incubation period, which at 2002 temperatures, would have been between eight and 25 days.

The third of the additional hypotheses explored was the temporal and spatial trends in human WNV disease onsets, mosquito density and IRs. In Figures 2.13 A and B, higher mosquito densities appeared to be in older urbanized counties with major metropolitan areas such as the Cuyahoga County/Cleveland and Hamilton

County/Cincinnati areas. Temporal and spatial trends in human WNV disease onsets, mosquito density and IRs seemed to be less apparent, although Hamilton, Cuyahoga, and

Lorain Counties had IR peaks at week 32 in 2002, one week before the state average, week 33; Cuyahoga and Lorain Counties had peak IRs at week 36, one week before the state average of week 37 in 2003; and Cuyahoga County reached the peak of human case onsets earlier than comparable large urban metropolitan counties.

The results reported prompted additional questions, and some concerns: 1) Did individual county mosquito control program methods affect WNV hazards in 2002 and

2003? 2) Why were there two peaks of bird deaths between weeks 26 and 31 and week

32? 3) Did the rate of change of DL affect mosquito diapause? 4) Were the ecological factors that informed the time-delayed indices in this study statistically significant in predicting the variation in IR? 5) Was there a biological reason which facilitated the

Blue Jay recovery, compared to the rate of the crow recovery? 6) How do the Ohio

“other” host species (grackles, robins, House Sparrows, and the cardinal) which were

120 prominent in 2003 compare to data found in other studies? And, 7) Did increases in mosquito density and bird deaths drive the increases in IRs, and did density decreases contribute to the decreases in IRs? Further studies will be needed to answer these questions.

This study was limited to a state and county level of data aggregation, which may not reflect WNV transmission risk at the individual county-level or at street-level transmission foci, respectively. An additional limitation was that the time-delayed indices hypothesized as drivers of IR were from observation only and not statistically significant indicators. A strength of the study was the calculation of individual sample-level IRs which were then averaged by week at the state or county level for analysis, which gave a less biased estimation of IR. An additional strength was that the time-delayed indices were informed with high resolution data from 20 weather stations, 60 CBCs, 10 weather districts, and weekly average DL data compiled from three separate Ohio areas.

2.6. Conclusion

The time-delayed indices which appeared to be related to increased mosquito IRs in 2002 will need further analysis to determine if they are statistically significant drivers of the variation in mosquito IRs. The DL indices were not included in the conclusion of this study because DL naturally decreased throughout the mosquito season, even during the weeks of highest IRs. Although ranges of temperature, precipitation, and the PDI changes were given as conclusions from the descriptive analyses, the results should be considered as qualitative analyses of the hypotheses. This study provided the insight to further understand the drivers of the transmission of WNV hazards from the mosquito to a human health risk and raised questions for further study. 121

Chapter 3: A statistical analysis of biotic and abiotic drivers associated with West Nile

virus mosquito infection and West Nile Virus human disease in Ohio, 2002-2006

3.1 Abstract

A historical database of human West Nile virus (WNV) disease and mosquito surveillance data from 2002-2006 in Ohio was accessed to study the drivers associated with WNV transmission in mosquitoes and humans. Mosquito infection rates (IRs) were calculated using minimum infection rate (MIR) and maximum likelihood estimate (MLE) methods. Indices were developed which were informed with weekly cumulative precipitation (CP), weekly mean temperature (T), and the Palmer Drought Index (PDI), representing time frames in the life cycle and ecology of the mosquito relative to the trapping week. Using a zero truncated negative binomial mixed regression model, IRs were found to be significantly associated with human WNV disease case onsets in two weeks time from the trapping week. Using a mixed model linear regression, the following conditions were significantly associated with increases in IRs and could be considered when planning mosquito control efforts: 1) an increase in T during the oviposition (OVP) index, the gonotrophic (GON2), egg-to-hatch (E_H3), oviposition site development

(OVPSD1), preoviposition site development (POVPSD), and overwintering time-delayed indices; 2) a decrease in CP during the E_H3 and OVPSD1 time-delayed indices; 3) an increase in CP during the POVPSD time-delayed index; and, 4) a decrease in the PDI

122 during the OVPSD1 and POVPSD time-delayed indices. Also, an increase in mosquito

IR was found to be associated with a significant increase in WNV infection in humans within two weeks after the week of trapping.

3.2 Introduction

WNV belongs to the Flaviviridae family of arboviruses (arthropod-borne viruses), which are distributed worldwide. Dengue and Yellow Fever belong to the same family, as do St. Louis Encephalitis (SLE) and Japanese Encephalitis (JE). Arboviruses are maintained in a complex enzootic cycle of least one non-human primary vertebrate host and a primary arthropod vector. Ecological conditions, particularly the relationship of the vector, host, and pathogen with temperature, precipitation and vegetative patterns, can both mediate or unleash the enzootic cycles of each arbovirus. Epizootic and epidemics occur when this enzootic transmission cycle becomes unbalanced, usually triggered by ecological changes. Competent vertebrate hosts and primary or secondary vectors amplify the virus and transmit it to human or equine hosts (Andreadis, Anderson,

Vossbrinck, and Main, 2004; Gubler, 2001; Kilpatrick, Kramer, Jones, Marra, and

Daszak, 2006; Mostashari, Kulldorff, Miller, and Kulasekera, 2003; Ruiz, Walker, Foster,

Haramis, and Kitron, 2007; Ruiz et al., 2010). WNV disease in humans may cause systemic febrile illness, meningoencephalitis, and death. About 0.66% of people infected develop severe illness. About 20% of people infected develop flu-like symptoms. About

80% of people infected will be asymptomatic (Centers for Disease Control and

Prevention, 2013).

Ohio mosquito program managers in local and state health departments and local mosquito control districts had two years from the WNV epidemic in New York City in

123 1999 to plan for the epizootic and epidemic. In 2001, the Ohio Department of Health

(ODH) began testing dead birds and adult mosquitoes for WNV. By 2002, Ohio was experiencing a full-scale WNV epidemic and epizootic. To prepare for and manage this re-emerging arbovirus, Ohio’s local health departments and mosquito control districts either developed new mosquito control infrastructure or redeveloped and bolstered existing infrastructure. While practitioners, researchers, and state and local public health agencies redesigned shelved mosquito-borne disease programs, state-level policy makers and legislators adopted funding strategies.

Depending on local support, Ohio health departments and mosquito control districts serving counties, cities, townships, and villages funded various levels of mosquito surveillance and control throughout the state, which in some areas, may have effectively reduced disease incidence. The newly redeveloped mid-20th century mosquito control programs included integrated pest management (IPM) methods safer to humans and the environment, which included public education, surveillance, source reduction, larval control, and adult mosquito control using chemicals sprayed from truck-based ultra-low volume (ULV) sprayers as the last public health protection strategy supported by the best available science (Marfin et al., 2001).

The data collected and analyzed in this study were a result of a large-scale mosquito and human surveillance and testing efforts throughout the state as part of the implementation of the ODH’s mosquito control strategy (ODH, 2003). The availability of this historical database was indispensable to this research. Unlike previous Ohio studies, this research used statistical regression methods to analyze the observational data from the state of Ohio during 2002-2006, to estimate the biological and meteorological drivers

124 underlying WNV infections in mosquitoes and humans using regression modeling methods, which integrated information that was learned from the descriptive study found in Chapter 2 and practical experience. The potential drivers included meteorologically- based indices that were informed with CP, T, and the PDI, which represented time frames in the life cycle and ecology of the mosquito relative to the trapping week.

This study attempted to answer the question: What are the biological and meteorological determinants of WNV infection in mosquitoes and humans? The hypotheses explored in this research were that significant increases in IRs will be associated with: 1) an increase in T during the OVP index, and the GON, E_H, OVPSD, and overwintering time-delayed indices; 2) a decrease in CP during the E_H, OVPSD, and the overwintering time-delayed indices; 3) an increase in CP during the POVPSD time-delayed index; 4) a decrease in the PDI during overwintering, OVPSD, and the

POVPSD time-delayed indices; and, 5) an increase in mosquito WNV IR will be associated with a significant increase in WNV infection in humans within two weeks after the week of trapping.

The specific aims of this research were to use an extensive empirical dataset from the state of Ohio to estimate the biological and meteorological drivers underlying WNV infections in mosquitoes and humans using regression modeling, and to integrate information learned from previous descriptive studies. The initial set of significant indices to be added into the model will result from a univariate analysis. Indices that significantly explain the variation of mosquito WNV IRs will be included in the final model. These indices will be used to inform further mathematical modeling studies of the dynamics of WNV transmission in Ohio.

125 3.2.1 Weather-based drivers of WNV transmission in mosquitoes from field

observations

In 2004, the World Health Organization (WHO) was not confident that climate- based models would have any predictive accuracy of human WNV compared to animal surveillance, but conceded that climate variables should be considered if they are “shown to be important” (Kuhn, Campbell-Lendrum, Haines, and Cox, 2004). Lardeux, Tejerina,

Quispe, and Chavez (2008) contemplated the nine years since WNV entered New York

City and the Western Hemisphere, and summarized what was learned regarding the effect of temperature and precipitation on WNV amplification in hosts and vectors. The term

“deconvolution” was used to describe what was needed to understand the complex, interrelated influences of climate-based temporal and spatial drivers. Current hypotheses were reiterated that suggest climate plays a crucial role in WNV transmission dynamics and that “weather reports could help preempt outbreaks through timely public warnings and supplemental mosquito abatement.” An antithetical point was made about the fact that Maine is the only state, with cold winters and short cool summers, that there have been few human infections and little avian mortality or morbidity (Lardeau, Marra,

Kilpatrick, and Calder, 2008).

As far as the role of precipitation in viral transmission, Lardeau et al. (2008) asserted that precipitation affected standing water and soil moisture which, in ideal conditions, promoted and amplified mosquito egg, larvae, and pupae development, but that precipitation, if too much or too little and at the wrong time in the mosquito life cycle, disrupted the transmission cycle by affecting vector density. By 2010, Bustamante and Lord (2010) emphasized the need to not only use calculations of mosquito IRs, but to

126 monitor “changes in abundance of parous females, changes in relative abundance of the total mosquito population, temperature and rainfall patterns, that should be used in conjunction with estimated infection rates in assessing risk.”

Ruiz et al. (2010) studied the effects of weather conditions on Culex pipiens IRs

in northeast Illinois (the Chicago metropolitan area of Cook and DuPage Counties) for the years 2004-2007. Infection rate was quantified using an MIR method: number of positive mosquito pools divided total number of mosquitoes tested times 1000. Positive pools in batches of 50 Culex spp. were identified using Rapid Analyte Measurement

Platform (RAMP) and VecTest, with final verification of positive pools using reverse transcription polymerase chain reaction (RT-PCR). Pool data were averaged by “each of the 370 unique trap locations and by week” (Ruiz et al., 2010), and trap locations were geocoded by address. Raw MIR values were capped at 76.92, the 95th percentile of all weekly pools, to remove high outliers before spatial analysis. Weather data (temperature and precipitation) was retrieved from the National Oceanic and Atmospheric

Administration National Climatic Data Center website for 21-32 weather stations, the

U.S. Geological Survey for 18-41 stations (precipitation), and 25 stations (precipitation) maintained by the Illinois Water Survey (Ruiz et al., 2010). For temperature, a degree week (DW) calculation was used similar to the degree day (DD) formula: DW =

Tmean/week – Tbase if Tmean/week > Tbase; DW = 0 if Tmean/week ≤ Tbase. 1, 3, and 5-week moving averages were calculated for precipitation measures. Weather and MIR variables were further processed by finding the difference between the average weekly MIR and the average MIR for that week across all years, and the difference between weekly temperature and precipitation and their 30-year normal values. The unit of analysis for

127 differences in temperature, precipitation, and MIR was a hexagon grid. Time-lagged correlations and linear regression methods were used to quantify associations between mosquito infection and weather and to estimate the best fitting model, respectively. No description was given on the type of mosquito trap employed or the mosquito surveillance methodology (Ruiz et al., 2010).

One of the findings was that weather conditions predicted 80% of the variability of mosquito IRs with “drier conditions in the spring and wetter conditions just prior to an increase in infection rates were factors” (Ruiz et al., 2010). The results from Ruiz et al.

(2010) that related to this dissertation research were the lagged associations found between temperature and precipitation and mosquito infection rate. Descriptive temporal graphics depicted a strong negative correlation between mosquito infection rate and precipitation about 10-12 weeks earlier (a decrease in precipitation was correlated with an increase in infection rate) for years 2004, 2005, and 2006. For 2007, a positive correlation between mosquito infection rate and precipitation occurred about ten weeks earlier (an increase in precipitation was correlated with an increase in infection rate). There was also evidence of a positive correlation between an increase in infection rate and precipitation

1-3 weeks earlier. Aggregating four years of data produced a “negative correlation at the

11-week lag using a 5-week moving average” (Ruiz et al., 2010). The highest Pierson’s r2, 0.8, was recorded for the correlation between temperature and infection rate when

Tbase reached 22°C and the accumulated DW was one week prior to the peak infection rate (DW was strongly correlated when it lagged behind infection rate by 1week). A statistical analysis was used to determine the significance of: 1) DW one week prior to the peak infection rate, 2) an 11-week lag precipitation (11 weeks prior to peak infection

128 rate) using a 5-week moving average, 3) a 3-week lag precipitation with a 3-week moving average, and 4) the previous year’s annual precipitation. With an r2 of 0.7, annual precipitation, DW, and 3-week lag precipitation with a 3-week moving average, were significant at α = 0.05 (Ruiz et al., 2010).

3.2.2 Weather-based drivers of WNV transmission in humans from field observations

Seven statistical studies were found in the literature that used temperature and precipitation as potential drivers of human WNV cases, and were in areas of the United

States (U.S.) that supported Culex pipiens role, among others, in enzootics, epizootics, and epidemics (Andreadis, et al., 2004; DeGroote, Sugumaran, Brend, Tucker, and

Bartholomay, 2008; Liu et al., 2009; Miramontes, Lafferty, Lind, and Oberle; 2006;

Nielsen, et al., 2008; Soverow, Wellenius, Fisman, and Mittleman, 2009; Winters, et al.,

2008).

DeGroote et al. (2008) analyzed with an unspecified statistical method, by a census block group unit of analysis in Iowa, U.S., from 2002-2006, annual mean temperature and precipitation at a 4 km2 of data resolution, mosquito density and WNV

IRs at a county-level of data resolution, and human disease (n = 298) at a census block group-level of data resolution (Tu and Ko, 2008), to test for associations with the presence of human WNV cases. The study length each year was from late May to

October. In 2003, block groups that had human WNV disease compared to block groups with no human disease had significantly lower temperature and less precipitation during the year of incidence and the prior year. WNV positive Culex pipiens and Culex tarsalis mosquitoes and human WNV disease cases temporally clustered in weeks 34-38, and

Culex tarsalis spatially clustered with WNV human disease in the Western Iowa. Fifety

129 seven positive pools were found out of 3,240 pools tested, with 41 positive pools composed of Culex pipiens (includes Culex restuans, both urban species) and 13 positive pools of Culex tarsalis (the rural Culex species) (DeGroote et al., 2008). Weekly human

WNV data were collected from the Iowa Department of Health disease surveillance system by week for census block groups with a population of over 500. Raster surfaces of temperature and precipitation were obtained from the Oregon State University Parameter- elevation Regressions on Independent Slopes Model (PRISM) website. Weekly mosquito density data were obtained from the Iowa-Mosquito.net website by week, which contained 35 years of historical New Jersey Light Trap (NJLT) data from 31-33 sites in

13 counties per year, and mosquito IRs from the Iowa State University Medical

Entomology Laboratory. No description was given on the method of calculation used to estimate mosquito IRs, the method of testing for WNV positive mosquitoes, the number of mosquitoes per pool tested, if the mosquito trapping sites were fixed, random, or foci- centric, the source of the climate data, whether the raw climate data were annual, monthly, or weekly, the frequency of mosquito surveillance, or the types of surveillance systems used to collect and manage human WNV or mosquito data (DeGroote et al.,

2008).

Liu et al. (2009) interpreted three separate logistic regression models using

Connecticut, U.S., town-level data resolution, with town as the unit of analysis and differing covariates to determine their association with WNV risk: model 1) land use variables, climate and population density; model 2) biotic data (dead bird reports and

Culex pipiens mosquitoes); and, model 3) both abiotic and biotic data. For the study years

2000-2005, 44 human cases in 169 towns were reported. Sixteen Connecticut weather

130 stations were assigned to the nearest town to minimize linear distances. For each town and each day per year, a 30-day trailing average and cumulative growing degree days

(GDD) were calculated. GDD were calculated as follows: Σ(over calendar year)[( Tmax +

o Tmin/ 2)] − Tbase, where Tbase = 10 C. Daily human WNV cases were collected from the

Connecticut Department of Public Health (CDPH) passive (active for human WNV disease between July and September) disease reporting system. Daily mosquito density, infection rate, and species were collected from a database of 91 fixed historically significant statewide locations from June to October each year, housed within the

Connecticut WNV Surveillance Program at the CDPH. Mosquitoes were tested in pools of up to 50 individuals. These locations were primarily in the more densely populated towns of Fairfield, New Haven, and Hartford Counties. NJLT and gravid traps were used to collect mosquitoes every ten days. Culex pipiens and Culex restuans were among the five species collected, but no relative abundance figures were presented. Daily dead bird sightings, mortality, and infection rate data by species were collected from 2000-2005 from the same database (Liu et al., 2009).

Liu et al. (2009) found that average temperature and precipitation in the previous

30 days to a case (reported to a passive surveillance system), growing degree days, finding a positive bird in the 30 days prior to a case, human population density, and finding a positive mosquito 30 days before a case were all significantly associated with an increase in human WNV infections in model 3. The results of model 2 indicated that the abundance of Culex pipiens had the largest influence on human WNV disease compared to the other mosquito species. Other “predictor” variables associated with an increased risk of human WNV disease were the presence of a WNV positive mosquito in

131 a town during the previous 30 days, the reporting of a dead bird in a town over the past

30 days, as well as WNV detected in a bird found in the town over the past 30 days, compared to an area where no trapping had been done. The significance of GDD in model 3 should be questioned because of a reported odds ratio of 1.00, a 95% confidence interval (CI) of (1.0, 1.0) and a p-value of < 0.0001. Also, no indications were given for the method used to estimate mosquito IRs or to test for WNV infection in mosquitoes.

Soverow et al. (2009) used a case-crossover case-control design where cases served as their own controls, and a logistic regression odds ratio (OR) of county-level climate data for the years 2001-2005, from 351 first order weather stations applying coverage to 16,298 cases occurring in 667 counties in the U.S., covering 17 states

(Illinois, Pennsylvania, Michigan, Indiana, Ohio, South Dakota, North Dakota, Nebraska,

Montana, Wyoming, Idaho, Colorado, Texas, Louisiana, Arizona, California, and New

Mexico). Human WNV case onset data for June through August were obtained from the

CDC. Climate data were obtained from the National Oceanic and Atmospheric

Administration (NOAA). Weather stations were spatially paired to the nearest county.

Averages from all weather stations were used if there were more than one weather station in a county. If a county (with a human case) lacked a weather station, it was paired to the nearest weather station in another county. The temperature variable cumulative weekly temperature > 14°C was a calculation of DD as found in Ruiz et al. (2010), using 14°C at

Tbase. It appeared that the data were aggregated for a country (U.S.) unit of analysis.

Prevalence odds ratios (PORs) were used to estimate incidence rate ratios (IRR)

(Thompson, Myers, and Kriebel, 1998; Tripepi, Jager, Dekker, Wanner, and Zoccali,

2007).

132 An increase in mean weekly maximum temperature during each of the four weeks prior to disease onset was significantly associated with a 32% to 50% higher incidence rate. Similar increases in human WNV disease incidence rates (four weeks prior to disease onset) were noted for increases in cumulative weekly temperature >14°C and mean weekly mean temperature. An increase in mean weekly dew point temperature was found to be significantly associated 1-3 weeks before the onset of WNV disease.

Adjusting for mean weekly maximum temperature and mean weekly dew point temperature, a 20mm increase in cumulative precipitation 1-2 weeks prior to disease onset associated with a very slight (4%-8%) increase in disease incidence. One or more days of heavy precipitation (≥ 50mm in a single day) significantly associated with a 33% higher incidence of disease during the same week of onset and remained higher during the second and third week prior to onset. Heavy precipitation at ≥ 40mm and ≥ 30mm in a single day tempered the effect on incidence accordingly (Soverow et al., 2009).

Soverow et al. (2009) concluded that “our results, which were maximal after a lag of 1–2 weeks for both temperature and rainfall, map onto the period when they might best accelerate reproductive activity and the probability of an infectious bite. Time from oviposition to adulthood in Culex is approximately 8–12 days, and the EIP under laboratory conditions ranges from 4-12 days at 30°C and > 28 days at 18°C (Dohm,

O’Guinn, and Turell, 2002). The typical incubation period in humans is 2-6 days, stretching back as far as 14 days.”

Andreadis et al. (2004) compiled county-level mosquito infection rate data from a database of mosquito surveillance records from 91 fixed historical trapping locations from June through October for eight counties in Connecticut, U.S., for the years 1999 to

133 2003. NJLT and gravid traps were used to collect mosquitoes, and the most abundant species collected were Culex pipiens and Culex restuans. The number of mosquitoes per pool was 1-50. 52,499 mosquito pools were tested and 210 tested positive for WNV, with

42% of the positive pools originating from Culex pipiens. Mosquitoes were assayed using

Vero cell culture and positive cultures were identified by RT-PCR or by TaqMan RT-

PCR (Andreadis et al., 2004). Mosquito IRs were calculated using the MLE developed by

Biggerstaff (2003). Human WNV disease onset dates by county level data resolution were obtained from the CDPH (n = 24). Climate data were obtained from NOAA New

England and 22 “official” weather stations and used to calculate average monthly deviations from the “norm” (mean) from June through September. Using descriptive statistics, an increase in human cases and a decrease in temperature and rainfall deviations from normal from June through September were associated in most years and in 2002 and 2003. There were spatial associations between the increase in human WNV disease and increases WNV positive mosquitoes and a temporal association between onset dates of human WNV disease 2-4 weeks after the last mosquito virus isolation

(Andreadis et al., 2004). No information was given on the types of human and mosquito

WNV surveillance systems, the frequency of mosquito trapping, or the method used to apply weather station data to county units of analysis.

Winters et al. (2008) studied the effect of climate on human WNV cases in western and eastern Colorado from 2002-2006 at a zip code-level data resolution and unit of analysis. 3,703 human WNV cases were accessed from the Colorado Department of

Public Health and Environment (CDPHE) disease reporting system and 3,659 cases were included in the study within zip codes with a cooling degree day (CDD) > 20.7oC, which

134 is a “quantitative index demonstrated to reflect demand for energy to heat or cool houses and businesses” (National Weather Service, 2013). CDD reflects the demand for cooling energy and has a direct relationship with WNV risk, i.e. the risk of infection to WNV vectors is minimized at temperatures of ≤ 20.7°C, and increased at temperatures of

>20.7°C. There were no formulas or a Tbase threshold given for the calculation of heating degree days (HDD). No potential biotic (birds or mosquitoes) drivers were included in this study. Multivariate logistic regression at a zip code unit of analysis was the method used to analyze the predictor variables significance. The binary outcome variable was high human WNV disease quantified by ≥ 38.39 cases per 100,000 person-years in eastern Colorado and ≥ 25.75 cases per 100,000 person-years in western Colorado, because the incidence rate range per zip code was wider in eastern than in western

Colorado (Winters et al., 2008). Climate data was downloaded at a 2 km2 resolution from

Climate Source Inc., a for-profit web-based climate database vendor, and the following variables were used in the analysis: mean monthly and annual temperature and precipitation, and HDD, among others.

Winters et al. (2008) concluded that the selected models of “eastern and western

Colorado models included temperature-related variables as important drivers of high

WNV disease incidence.” This was not surprising given that warm temperatures resulted in more rapid development rates among the immature life stages and a shorter gonotrophic cycle in Culex tarsalis, as well as a shorter extrinsic incubation period of

WNV in Culex vectors. In eastern Colorado, the model containing only temperature in

March (parameter estimate = 0.05) was the best fit model. In western Colorado, the model containing HDD in August (parameter estimate = 0.03), precipitation in July

135 (parameter estimate = -0.06) and snow in September (parameter estimate = -0.07), along with elevation and normalized difference vegetation index (NDVI), was the best model

(Winters et al., 2008). Winters et al. (2008) explained the results and their affect on WNV disease incidence:

“Interestingly, in eastern Colorado, we found a negative association between areas with high WNV disease incidence and temperature in August (i.e., a positive association with heating degree days that reflects the demand for heating energy and therefore has a negative relationship with temperature). This may be explained by (1) high summer temperatures ultimately leading to reduction in availability of natural larval habitat in the semi-arid climate of eastern Colorado and (2) possible negative effects of high temperatures on WNV replication or transmission that previously was shown for western equine encephalitis virus. In contrast, the selected western Colorado model included a positive association between high WNV disease incidence and temperature in March. This association indicates that early season warm stretches are critical for mosquito population build-up and sets the stage for WNV disease outbreaks in the mountainous, high-elevation landscape of western Colorado” (Winters et al., 2008).

Precipitation in July was found to be negatively associated with an increase in human

WNV disease incidence. An increase in precipitation (described as “summer rains”) was theorized to have the effect of washing out eggs and larvae from habitats needed for the development of eggs to adult mosquitoes.

The Winters et al. (2008) models were validated internally on the same dataset used to build the model and externally on a dataset not used in model building. The model obtained 80% accuracy in western Colorado and 67% accuracy in eastern

Colorado for predicting zip codes with high incidence for areas with high incidence zip codes, but was only 26-41% accurate when applied to the opposite side of the state, which lead to the conclusion that the model was more valid when applied to the model

136 development area (Winters et al., 2008). There was not a description of the number and distribution of weather stations relative to human WNV cases.

Miramontes et al. (2006) compiled human WNV data for 287 counties in

Colorado, Nebraska, Louisiana, and Pennsylvania for the years 2002 and 2003 at a county-level data resolution from state websites. Climate data was collected from the

Oregon Climate Service’s Prizm Data Explorer. Average temperature (°F) was calculated for April through October along with total annual precipitation. With a unit of analysis at the county-level, two logistic regression models were tested, one with a dependent variable of the presence or absence of a human WNV case in a county (n = 287 counties), and the second with the presence of at least ten human WNV cases in a county (n = 158 counties in text, n = 98 counties in Table 2) compared to no cases in a county

(Miramontes et al., 2006). In counties with at least one human WNV case, it was 1.19 times more likely to have an increase in temperature by 1°F than in counties with no human WNV case, and in counties with at least ten human cases it was 2.38 times more likely to have an increase in temperature than in counties with no human cases

(Miramontes et al., 2006).

Nielsen et al. (2008) studied abiotic and biotic risk factors of human WNV in

Davis, California in 2006. Human WNV cases were accessed from the Yolo County

Health Department by date of onset and census block data resolution, with 14 of 15 human cases in 2006 occurring during July and August. Adult mosquitoes were collected and processed from 21 NJLT and gravid trap sites set in a grid formation of seven north- south transects of three traps each set 1.5 km apart. During the study period the

Sacramento-Yolo Mosquito and Vector Control District (SYMVCD) performed aerial

137 spraying for adult mosquitoes over the study area on August 8 and August 9, 2006. Dead birds reported to the California Department of Health Services dead bird hotline were collected and sent to the California Animal Health and Food Safety laboratory for processing and the Arbovirus Research Unit of the Center for Vector-borne Diseases

(CVEC) laboratory at the University of California, Davis, for testing using RT-PCR.

Mosquitoes were collected one night per week from April to mid-October, sorted into pools of ≤ 50 mosquitoes, and tested by multiplex RT-PCR capable of detecting 10 PFU per 0.1ml. Thirty seven of 144 (32.5%) dead birds and 16 of 1355 mosquito pools tested positive for WNV. Of the mosquitoes caught in gravid traps, 61.9% were Culex pipiens.

Climate variables, daily minimum and maximum temperature and precipitation were recorded at a weather station located in Davis and the data was downloaded from the

California Integrated Pest Management project website (http://www.ipm.ucdavis.edu)

(Nielsen et al., 2008). DDs were calculated using 14.3°C as Tbase, and a DD of 109°C was used to estimate the length of the EIP from field data, beginning at the first indication of

WNV in a dead crow. Nielsen et al. (2008) was able to predict two epizootic peaks of

WNV transmission in mosquitoes through three EIP cycles. The EIP cycles were 12 days,

9 days, and 7 days from June 28 to July 27, 2006, decreasing due to increasing temperatures. This finding was important to the prediction of mosquito competence and its affect on the potential of human WNV risk.

138 3.3 Methods

3.3.1 Study area

The study area included counties in the state of Ohio that had mosquito surveillance programs and human WNV disease reported to the ODH, with emphasis on the older urbanized metropolitan areas of Franklin County/Columbus City, Hamilton

County/Cincinnati, Lucas County/Toledo, Lorain County, and Cuyahoga

County/Cleveland. These areas had the highest number of WNV positive mosquito pools and/or the highest incidence of WNV human cases in the state during the 2002 and 2003 epizootic/epidemic.

3.3.2 Human WNV case onset data, collection and management

Ohio human WNV probable and confirmed non-neuroinvasive and neuroinvasive disease data were accessed for the years 2002-2006 from the Ohio Disease Reporting

CDC case definition.

Local health departments from 68 of 88 (77%) counties reported 669 human

WNV disease cases from 2002-2006. Eighty seven of 127 (~69%) Ohio city and county health departments had human cases within their jurisdictions. Ten of 68 (14.7%) counties had greater than or equal to ten cases from 2002-2006, with a range for all counties of one to 285. The median number of cases for all counties was 3, the mode was

1, and the geometric mean was equal to 3.8. Appendix D contains a table of the Ohio counties with human cases from 2002-2006. Each human case onset date was converted

139 to an onset week, and the total number of cases per week were aggregated by county and year. Table 3.1 summarizes the 2002-2006 human neuroinvasive WNV cases and deaths for Ohio (CDC, 2013; ODH, 2010).

Total Year Human Neuroinvasive Deaths 2001 Cases0 0 0 2002 441 310 31 2003 107 84 8 2004 12 11 2 2005 61 46 2 2006 48 36 4 Total 669 487 47 Table 3.1. Total Ohio human WNV cases, neuroinvasive cases, and deaths by year, 2002-2006

3.3.3 Mosquito WNV infection rate data, collection and management

Mosquitoes were collected by local health departments, mosquito control districts, and local communities operating an adult mosquito surveillance program throughout

Traps were placed overnight (i.e., trap night), and specimens were collected the next day, usually as soon as possible after sunrise. Depending on the jurisdiction, the number of

140 locations varied from one to over 30 each trap night. The number of trap nights per week varied among jurisdictions, with the one or two trap nights being the norm and three per week the exception, because of the intensity of the labor necessary and subsequent costs incurred with each collection. Once collected, specimens from each trap were frozen, and treated as independent samples. Specimens were then counted, and males and females were separated, Culex spp. were identified and placed in pools of 50 or fewer individuals, and the remaining species were pooled accordingly. Each pool was identified by trap number and shipped or driven to the Vector-Borne Disease Program (VBDP) laboratory for real-time RT-PCR testing for the WNV per the standard methods of RNA extraction in Mans et al. (2004). Positive and negative pool results were recorded by lab technicians in an electronic spreadsheet. All positive results were given to the local jurisdiction by phone or e-mail message as soon as the test was completed. Each month, an updated arbovirus report in the form of the spreadsheet was sent by e-mail to mosquito control managers (ODH, 2005).

Cycle threshold (CT) values were recorded by hand in a hard-copy lab notebook per mosquito pool number for years 2003-2006 and matched with the unique pool number. Records for 2002 could not be found in storage. The parameter CT is defined as the cycle number required for dye fluorescence to become higher than background fluorescence level. The method is based on the inverse exponential relationship that exists between the initial quantity (copy number) of target sequence copies in the reactions, and corresponding CT determinations, i.e., the higher the starting copy number of the RNA target sequence, the lower the CT value (Mackay, Arden, and Nitsche, 2002).

Any sample with less than a threshold cycle value of 35 was considered positive.

141 Mosquito infection data for the years 2002-2006 were accessed by direct written request to personnel in VBDP managing Ohio’s historical arbovirus database. The data were contained in a spreadsheet in which each row contained a unique pool identification number assigned by the lab technician who performed the real-time RT-PCR test. The columns on the spreadsheet included data for the collection date and a trap identification number assigned by the surveillance agency, the species collected identified by VBDP lab personnel, the number of mosquitoes in each pool determined by the surveillance agency, the county of collection, the surveillance agency that submitted the specimens for testing, real-time RT-PCR results (+/- for WNV), date tested, and year tested, all recorded by lab personnel.

Mosquitoes were submitted from 75 of the 88 (81%) counties in Ohio from 2002 to 2006 (no mosquitoes were submitted from 13 counties). Thirteen of the 75 (16%) counties with mosquitoes submitted for testing had no human WNV cases. Only negative mosquitoes were submitted from 17 counties out of 75 (58 counties had negative and positive mosquitoes), and 12 of the 17 counties had human cases reported (five had no human cases reported). Mosquitoes were not submitted from seven counties with human cases reported. Appendix E contains a table of the mosquito species tested for WNV, the number of individual mosquitoes tested for each species, the number of mosquito pools tested per species, the number of positive pools, and the proportion of positive pools.

The proportion of Culex spp. to the total number of mosquitoes collected from 2002-2006 ranged from 94%-96%. The proportion of positive mosquito pools of all species to the total number of mosquitoes collected and tested was 30% in 2002, 6% in 2003, 7% in

142 2004, 12% in 2005 and 8% in 2006. 55 different species of mosquitoes were collected and identified throughout the state.

Table 3.2 contains the number of mosquitoes of all species that tested positive for

WNV (CDC, 2013; ODH, 2010) from 2001-2006. Table 3.3 is a statewide summary of the number of pools tested, the number positive, the density, and the statewide aggregated

MIR for only Culex spp. during the years 2002-2006, showing an approximate three to five-fold decrease in MIR from 2002 to 2003-2006.

Mosquitoes Year Tested # Pools Pools + 2001 91,590 No data 26 2002 187,046 8,246 2082 2003 490,249 19,796 799 2004 398,832 14,202 874 2005 390,010 14,705 1373 2006 444,074 15,353 913 Total 2,001,801 72,302 6,067

Table 3.2. Total of all mosquito species tested, number of pools tested, and positive pools by year for Ohio, 2001-2006

# # Pos Year Pools pools Density MIR 2002 5669 1752 174652 10.0 2003 10840 724 361787 2.0 2004 10839 837 375304 2.2 2005 10769 1327 375799 3.5 2006 11569 896 420724 2.1 Total 49686 5536 1708266 3.2 Table 3.3. Number of Culex spp. pools tested, number of positive pools, number (density) of Culex spp. collected, and aggregated minimum infection rate (MIR) of Culex spp. for

Ohio by year, 2002-2006

143 Before IRs were calculated, all mosquito samples and pools caught from light traps were removed from the database to facilitate an analysis of only mosquitoes caught in gravid traps, with the exception of the Lake County light traps. To justify the inclusion of Lake County’s light trap data, Table 3.4 shows that the percentage of Culex spp. caught in Lake County, even though mosquito control managers used a trap with bait

(CO2) that attracted bridge vectors and mosquitoes attracted to mammalian hosts for blood meals, was similar when compared to gravid trap surveillance data.

County Density all species Density % Culex Cuyahoga 41083 40681 99 Franklin 8421 7206 86 Hamilton 23844 23100 97 Lake 12206 11666 96 Lorain 19456 19092 98 Lucas 953 806 85 Montgomery 3042 3037 100

Table 3.4. Cumulative density (count) of all species, density of Culex species, and %

Culex spp., for Cuyahoga, Franklin, Hamilton, Lake, Lorain, Lucas, and Montgomery

Counties in Ohio, 2002-2006

The database was also stripped of all mosquito species except Culex spp. or Culex pipiens, to ensure an analysis that is not biased by the breeding or feeding behaviors of other mosquito species. IRs were calculated by two methods: 1) MIR, the number of positive mosquito pools in each sample (one sample per trap and one or more pools per sample) divided by the total number of mosquitoes tested times 1000, 2) calculating the maximum likelihood estimate (MLE) for each sample (one sample per trap and one or

144 more pools per sample). The MLE is an estimate of the actual number of positive mosquitoes in a sample (using PooledInfRate software as an add-on to Excel, Biggerstaff,

2003). The sample MIR was used if every pool per sample was positive, which renders the differential equations used in the calculation of the MLE unsolvable.

After calculating the sample IRs, it was apparent that there were extreme outliers in the range of IRs for each sample, county, and week. These outliers were caused by very small sample sizes, such as one positive pool and a sample size of one, which calculated to an estimated MIR of 1000; one positive pool and a sample size of two calculated to an estimated MIR of 500, one positive pool and a sample size of four calculated to an estimated MIR of 250/1000, and two positive pools with a sample size of five calculated to an estimated MIR of 400/1000. Placing this phenomenon into perspective, for the year 2002, the state average MIR was 10/1000 for all counties. In some counties, the average IR ranged from 30-50/1000 during peak transmission.

To achieve an 80% probability of detection during peak transmission periods, sample sizes of 31 to 52 were found to be necessary (Gu and Novak, 2004). For the year

2002, 88 samples of size one to four mosquitoes out of 779 total pools (11%) were positive. In 2006, with a statewide MIR of 2/1000, only four samples of size one to four mosquitoes out of 793 (0.5%) were positive. Because extremely high sample MIRs upwardly biased weekly mean IRs, estimates of MIRs greater than 200/1000 were removed for every county, week, month, and year. Even though many estimates of MIRs were above the upper fence, the box plots in Figure 3.1 indicated a distinct separation of points above an MIR of 200/1000. After the removal of any significant outliers, IRs were

145 sorted and averaged by county, year, and week, and by week and year for further analysis.

3.3.4 Meteorological data, collection and management

Daily mean temperature and total daily precipitation data were downloaded from archived sources from the NOAA National Climatic Data Center, and Land Based Data

Sets from 20 primary weather stations located throughout Ohio (Appendix A) (NOAA,

2012), and converted to T (ºC) or CP (mm). Each county in Ohio was assigned to its closest weather station based upon distance and prevailing weather patterns in collaboration with a Columbus-based meteorologist (e-mail by Jym Ganahl, Chief

Meteorologist, Channel 4, Columbus, Ohio, January 25, 2013).

Cincinnati for the western portion (Appendix B). PDI data were downloaded from

NOAA U.S. climate monitoring weekly products (NOAA, 2012a) for each of the 10 Ohio weather districts (Appendix C).

Table 3.5 contains the estimated time-delays of the indices, which were constructed as time periods relative to the week mosquitoes were trapped (weeks before and during the trapping week). Specifically, these time periods estimated the temporal position of phases of the mosquito life cycle, and the ecological conditions necessary for development within these phases, in relation to the trapping week. These indices were oviposition, the egg, larvae and pupae stage (i.e., eggs-to-hatch), the adult stage (which

146

Minimum Infection Rate Outliers for Selected Ohio Counties, 2002 1,000

800

600 400

Mimimum infection infection rate Mimimum 200

Butler County Cuyahoga County Franklin County Hamilton County Lake County Lorain County

Summit County Lucas County Montgomery County Stark County

Figure 3.1. Minimum infection rate statistical outliers for selected Ohio counties, 2002

included the gonotrophic cycle and the extrinsic incubation period), two theorized time

periods that were needed for optimum oviposition site development, and an

overwintering time period from December to March. These indices were informed by T

and CP data, the weekly PDI, and DL (photoperiod). Multiple time periods were

established for some indices to evaluate which one may have had a statistically

significant association with mosquito and human WNV infections. A "-" symbol

preceding the week number indicated a week prior to the trapping week (i.e., backwards

in time) and a "+" symbol preceding the week number indicated the week of trapping that

was the same week as the estimated OVP index. For example, to inform the E_H time-

delayed indices E_H, E_H1, E_H2 and E_H3 with T, T data from weeks -2 and -3 were

added, and the sum was divided by 2, T for week -2 was used, T for week -3 was used,

and T data from weeks -3 and -4 were added, and the sum was divided by 2, respectively.

To inform the GON time-delayed indices GON, GON1, and GON2 with CP, CP data

147 from weeks +1 and -1 were summed, CP from week -1 was used, and CP data from weeks -1 and -2 were summed, respectively. The 7-day PDI was defined by NOAA as follows:

The PDI is calculated based on precipitation and temperature data, as well as the local Available Water Content (AWC) of the soil. From the inputs, all the basic terms of the water balance equation can be determined, including evapotranspiration, soil recharge, runoff, and moisture loss from the surface layer. Human impacts on the water balance, such as irrigation, are not considered. Complete descriptions of the equations can be found in the original study by Palmer (NOAA, 2012).

Index code Index Description Index Time-Delay OVP Oviposition Week +1 GON Gonotrophic Cycle Week +1 : -1 GON1 Gonotrophic Cycle1 Week -1 GON2 Gonotrophic Cycle2 Week -1 : -2

E_H Eggs-to-Hatch Week -2 : -3 E_H1 Eggs-to-Hatch1 Week -2

E_H2 Eggs-to-Hatch2 Week -3 E_H3 Eggs-to-Hatch3 Week -3 : -4 OVPSD Oviposition Site Development Week -5 : -7 OVPSD1 Oviposition Site Development1 Week -6 : -8 POVPSD Preoviposition Site Development Week -8 : -11 POVPSD1 Preoviposition Site Development1 Week -9 : -12 OW December to March (Overwinter) December, January, February Table 3.5. Estimated time-delays of the indices representing the mosquito life cycle and ecology which were constructed as time periods relative to the week mosquitoes were trapped (week +1 is the trapping week)

Figure 3.2 is a graphical illustration of the estimated time-delays of the indices relative to the week mosquitoes were trapped. The week of trapping is indicated by week

+1. The trap week (TW) was assumed to be the week of oviposition. Human exposure to

148 an infectious mosquito was assumed to have occurred during the GON time-delayed indices between weeks -2 to +1.

E-H2 E -H1 GON

POVPSD1 OVPSD1 E-H GON1

OW POVPSD OVPSD E-H3 GON2 OVP

-56 -52 -48 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 +1 Weeks before trap week (TW) TW

Oviposition (OVP) estimated time period Gonotrophic (GON) estimated time-delay Eggs-to-hatch (E-H) estimated time-delay Oviposition site development (OVPSD) estimated time-delay delaysPreoviposition site development (POVPSD) estimated time-delay

Overwinter (OW) estimated time-delay

3.3.5 Analytical methods

A total of 339 weeks of human case onsets were available for analysis after human case onset dates were converted to total weekly onsets by county and year. 418,

481, 509, 387, and 332 weeks of average IRs by county, year, and trap week were available for analysis for 2002, 2003, 2004, 2005, and 2006, respectively. To evaluate whether there was a significant relationship between human cases and IR, four distinct

149 spreadsheets were created in Microsoft Word Excel that were integrated by county, year and week. IRs were averaged weekly from mosquito surveillance data, and human case onsets were cumulative by week. Each spreadsheet was constructed with a different and hypothetical temporal lag between IR and human case onsets of one, two, three, and four weeks. Human cases were not included on these databases without corresponding weekly mosquito surveillance data, i.e., a county had to have a human case reported to the ODRS and the mosquito control agency or agencies in that county had to report surveillance data to the ODH, before both IRs and human case onset data were integrated into the four databases. Because of this constraint, the databases that were analyzed included 164, 182,

190, and 184 weeks of IRs integrated with human case onsets lagged for one, two, three, and four weeks, respectively. The unit of analysis was weekly average IRs and human case onsets.

These four spreadsheets were also integrated by county, year, and trap week, and the ecological indices in Table 3.4 and Figure 3.2, and were combined into one spreadsheet with 435 distinct rows of data to evaluate whether there was a significant association between the indices and IR. The unit of analysis was weekly average IRs and ecological indices.

For the analysis of human WNV cases and IRs, a density distribution of number of weeks was plotted of human case onsets per week (i.e., one case onset occurred in 254 weeks), which assumed a negative binomial distribution with a variance equal to

1612.551 and a mean equal to 8.27 (Figure 3.4 expands the y-axis between 0 and 51 of

Figure 3.3).

150

Figure 3.3. Number of weeks (y-axis = 0-254) by number of cases per week (x = 0-41),

Ohio, 2002-2006

Figure 3.4. Number of weeks (y-axis = 0-51) by number of cases per week (x = 0-41),

Ohio, 2002-2006

For the analysis of IRs and ecological indices, a density plot showed that the distribution was non-normal and right-skewed (Figure 3.5). Based upon this distribution, which was caused by an over-dispersion of zero IRs, an IR of 1/1000 was added to all

151 IRvalues and then these data were log-transformed (Figure 3.6), which achieved a

normal distribution.

Mosquito Infection Rate Density

.05

.04

.03

Density

.02

.01 0 0 50 100 150 Infection Rate/1000

Figure 3.5. Mosquito Infection Rate Density

Mosquito Log Infection Rate Density

Density

.2 0 0 1 2 3 4 5 Log Infection Rate

Figure 3.6. Mosquito Log Infection Rate Density

152 A zero truncated negative binomial mixed model regression statistical method was applied using Statistical Analysis Software (SAS) to estimate the significance of the association between IRs and human WNV, with IRs being the predictor variable. The following equation models the zero truncated mixed effect negative binomial regression: ln(Yij) = β0 + β1 x(IR) + μj + ϵij , where ϵij is random error or the amount of variability in the weekly human case onsets for each county around the weekly average line for that county. μj is the random intercept or the random effect of the county on this weekly average and assumed to be independent.

The zero truncated model was used because of over-dispersion and because it is not possible to report “0” human WNV cases to the Ohio Infectious Disease Reporting

System (zero truncated models are typically applied in studies where there is over dispersed data and when it is impossible to have “0” counts, such as a study of hospital stays in days when a “record would not appear in the dataset if a patient had not gone to the hospital and it would be impossible to have a record of “0” for no hospital stay”

(http://www.ats.ucla.edu/stat/stata/output/Stata_ZTNB.htm, March 4, 2014)).

The statistical method used in this study to estimate the significance of the association between the predictor indices and the IRs was a mixed model linear regression using STATA v10 software with the “xtmixed” command. The following linear regression model was specified as lnIR = β0 + β1x1ij +… + βpxpij + ξij where x1ij through xpij are the predictor variables(indices) and ξij is a residual. The linear random intercept model with covariates, splits the total residual (error) into two levels or error components: 1) ζj, the random effect of counties, or the between county level-2 residual, is the random deviation of the individual counties mean IR from the overall statewide

153 mean, and 2) ϵij, the level-1 within county random deviation of weekly IR values, from the individual counties mean: ξij = ζj + ϵij, resulting in the following model: (β0 + ζj) +

β1x1ij +… + βpxpij +ϵij. The model can be considered a regression model with an added residual, ζj, or a county-specific intercept (β0 + ζj). The model’s random intercept, ζj, is not estimated with the fixed parameters, but is estimated with the variance of ϵij. ζj is shared between weeks in the same county, and ϵij is unique for each week. The linear random intercept model is the simplest form of a linear mixed (effects) model where there are both fixed and random effects. These two components account for the unrealistic assumption that the weekly IRs from the same county are uncorrelated and independent. The model meets the independence assumption because the level-2 county specific error component remains constant over all weeks, whereas the level-1 residual is a week-specific error component, which varies between weeks and counties. ζj is uncorrelated over counties, ϵij is uncorrelated over counties and weeks, and ζj and ϵij are uncorrelated with each other (Rabe-Hesketh and Skrondal, 2012).

Statistical modeling was performed within each index (biological/ecological phase) to determine if there were any significant associations between IR and the time- delayed indices. For instance, within E_H, each of the four time-delayed indices were informed with T (TE_H, TE_H1, TE_H2, TE_H3) and CP (CPE_H, CPE_H1, CPE_H2,

CPE_H3). Then all possible combinations of T and CP indices with significant univariate coefficients were combined into multivariate models for each life cycle or ecological phase to find the best fitting model containing both indices. For ecological phases such as oviposition with three time-delayed indices, all possible indices with significant

154 univariate coefficients were combined into multivariate models to find the best fitting model containing all three indices.

3.4 Results

3.4.1 The relationship between WNV in humans and infection rate

Table 3.6 documents the coefficient estimates of the zero-truncated negative binomial mixed model. In every analytical scenario, IR was significantly associated with human WNV case onsets. The lowest estimated P value of all the lag periods, <0.0001, was from the two and three-week lag analysis. Of these two models, the best fitting model indicated by the lowest gradient value of the coefficient for parameter b1, was the

2-week lag model.

1-Week Lag IR Parameter Estimate SE DF t Value Pr>|t| Alpha Lower Upper Gradient b0 -3.1201 0.9019 29 -3.46 0/0017 0.05 -4.9648 -1.2755 1.10E-03 b1 0.05737 0.01283 29 4.47 0.0001 0.05 0.03113 0.08361 2.58E-02 sigma2 1.4811 0.7981 29 1.86 0.0737 0.05 -0.1512 3.1134 4.99E-04 alpha 3.2761 3.6239 29 0.9 0.3734 0.05 -4.1356 10.6877 2.34E-04 2-Week Lag IR Parameter Estimate SE DF t Value Pr>|t| Alpha Lower Upper Gradient b0 -2.8054 0.5817 34 -4.82 <.0001 0.05 -3.9875 -1.6233 9.86E-07 b1 0.05181 0.009352 34 5.54 <.0001 0.05 0.0328 0.07082 -0.00014 sigma2 1.6087 0.8752 34 1.84 0.0748 0.05 -0.1699 3.3873 -2.37E-07 alpha 1.7779 1.267 34 1.4 0.1696 0.05 -0.797 4.3528 2.06E-06

Table 3.6. Zero-truncated negative binomial mixed Continued model regression coefficient estimates

155 Table 3.6. Continued

3-Week Lag IR Parameter Estimate SE DF t Value Pr>|t| Alpha Lower Upper Gradient b0 -2.685 0.755 37 -3.56 0.0011 0.05 -4.2147 -1.1552 0.000205 b1 0.05553 0.01191 37 4.66 <.0001 0.05 0.0314 0.07965 -0.00657 sigma2 1.3793 0.7062 37 1.95 0.0584 0.05 -0.05168 2.8103 0.000059 alpha 3.2061 2.9902 37 1.07 0.2906 0.05 -2.8526 9.2648 0.000066 4-Week Lag IR Parameter Estimate SE DF t Value Pr>|t| Alpha Lower Upper Gradient b0 -3.8442 2.5151 32 -1.53 0.1362 0.05 -8.9672 1.2788 0.000012 b1 0.05101 0.01475 32 3.46 0.0016 0.05 0.02095 0.08106 -0.00008 sigma2 1.5432 0.7799 32 1.98 0.0565 0.05 -0.04546 3.1318 -0.00001 alpha 13.0997 35.5633 32 0.37 0.715 0.05 -59.3403 85.5398 1.05E-07

3.4.2 The relationship between WNV in mosquitoes and time-delayed indices

The coefficients of the univariate and multivariate statistical analysis between mosquito IR and predictor indices are documented in Tables 3.7 and 3.8. The most significant predictor indices (α < .05) were noted with “*” and the best fitting models with the lowest AIC or BIC were noted with “**”. The best fit models included the time period TOVP, the time-delayed indices TGON2, TE_H3 and CPE_H3, the model including PDIOVPSD1, TOVPSD1, and CPOVPSD1, the model including PDIPOVPSD,

TPOVPSD, and CPPOVPSD, and TOW.

156 Predictor Index (x1) Parameter Estimate Std. Err. z p>|z| 95% CI AIC BIC b0 -3.439 0.663 -5.18 0.000 -4.739 -2.138 1476.214** 1492.515** TGON2 b1 0.211 0.029 7.39 0.000* 0.155 0.267 sd(_cons) 0.472 0.102 0.309 0.719 sd(Residual) 1.255 0.044 1.171 1.345

b 0 1.475 0.139 10.62 0.000 1.203 1.747 1532.021 1548.323 CPGON2 b1 -0.002 0.002 -1.04 0.300 -0.006 0.002 sd(_cons) 0.500 0.111 0.323 0.774 sd(Residual) 1.330 0.047 1.241 1.425

b 0 -1.895 0.578 -3.28 0.001 -3.028 -0.762 1495.56 1511.861 TGON1 b1 0.144 0.025 5.8 0.000* 0.095 0.192 sd(_cons) 0.470 0.103 0.306 0.722 sd(Residual) 1.284 0.045 1.199 1.376

b 0 1.418 0.127 11.17 0.000 1.169 1.667 1532.225 1548.526 CPGON1 b1 -0.001 0.003 -0.41 0.679 -0.007 0.004 sd(_cons) 0.495 0.111 0.319 0.766 sd(Residual) 1.332 0.047 1.243 1.428

b 0 -2.016 0.670 -3.01 0.003 -3.329 -0.703 1501.768 1518.07 TGON b1 0.150 0.029 5.17 0.000* 0.093 0.207 sd(_cons) 0.503 0.107 0.331 0.763 sd(Residual) 1.291 0.045 1.205 1.383

b 0 1.523 0.139 10.94 0.000 1.250 1.795 1530.344 1546.645 CPGON b1 -0.003 0.002 -1.64 0.101 -0.007 0.001 sd(_cons) 0.498 0.111 0.322 0.769 sd(Residual) 1.328 0.047 1.239 1.423

b 0 -0.418 0.577 -0.72 0.469 -1.549 0.713 1517.821 ** 1534.122 ** TOVP b1 0.081 0.025 3.21 0.001* 0.031 0.130 sd(_cons) 0.504 0.109 0.330 0.772 sd(Residual) 1.315 0.046 1.227 1.409

b 0 1.490 0.127 11.77 0.000 1.242 1.738 1529.156 1545.457 CPOVP b1 -0.005 0.003 -1.82 0.069 -0.010 0.000 sd(_cons) 0.497 0.111 0.321 0.769 sd(Residual) 1.327 0.047 1.238 1.422

b 0 1.456 0.106 13.79 0.000 1.249 1.663 1462.048** 1478.35** TOW b1 0.230 0.027 8.42 0.000* 0.177 0.284 sd(_cons) 0.446 0.098 0.290 0.685 sd(Residual) 1.237 0.043 1.154 1.325

b 0 1.490 0.127 11.77 0.000 1.242 1.738 1529.156 1545.457 C POW b1 -0.005 0.003 -1.82 0.069 -0.010 0.000 sd(_cons) 0.497 0.111 0.321 0.769 sd(Residual) 1.327 0.047 1.238 1.422 Table 3.7. Univariate regression coefficient estimates Continued

157 Table 3.7 continued

b0 1.354 0.131 10.33 0.000 1.097 1.611 1526.45 1542.751 PDIOW b1 0.029 0.043 0.68 0.499 -0.056 0.114 sd(_cons) 0.492 0.111 0.317 0.765 sd(Residual) 1.332 0.047 1.243 1.427

b 0 -3.984 0.581 -6.86 0.000 -5.122 -2.846 1447.079 1463.38 TE_H3 b1 0.235 0.025 9.42 0.000* 0.186 0.284 sd(_cons) 0.453 0.097 0.298 0.690 sd(Residual) 1.214 0.043 1.133 1.300

b 0 1.625 0.137 11.83 0.000 1.356 1.894 1523.937 1540.238 CPE_H3 b1 -0.006 0.002 -3.05 0.002* -0.009 -0.002 sd(_cons) 0.496 0.109 0.322 0.762 sd(Residual) 1.318 0.046 1.230 1.412

b 0 -3.926 0.630 -6.24 0.000* -5.160 -2.692 1459.726 1476.028 TE_H b1 0.232 0.027 8.58 0.000 0.179 0.285 sd(_cons) 0.456 0.098 0.299 0.694 sd(Residual) 1.232 0.043 1.150 1.320

b 0 1.538 0.139 11.1 0.000 1.267 1.810 1529.664 1545.965 CPE_H b1 -0.004 0.002 -1.87 0.062 -0.007 0.000 sd(_cons) 0.499 0.111 0.323 0.771 sd(Residual) 1.326 0.047 1.238 1.421

b 0 -2.083 0.555 -3.75 0.00 0 -3.172 -0.994 1489.077 1505.379 TE_H1 b1 0.152 0.024 6.39 0.000* 0.105 0.199 sd(_cons) 0.449 0.101 0.290 0.697 sd(Residual) 1.277 0.045 1.192 1.368

b 0 1.451 0.128 11.33 0.000 1.200 1.702 1531.391 1547.692 CPE_H1 b1 -0.003 0.003 -1.03 0.303 -0.008 0.003 sd(_cons) 0.501 0.112 0.323 0.775 sd(Residual) 1.330 0.047 1.241 1.425

b 0 -2.520 0.534 -4.72 0.000 -3.566 -1.474 1475.361 1491.663 TE_H2 b1 0.171 0.023 7.49 0.000* 0.126 0.216 sd(_cons) 0.445 0.098 0.289 0.686 sd(Residual) 1.256 0.044 1.173 1.346

b 0 1.483 0.126 11.73 0.000 1.236 1.731 1529.868 1546.169 CPE_H2 b1 -0.004 0.003 -1.62 0.106 -0.010 0.001 sd(_cons) 0.488 0.109 0.315 0.757 sd(Residual) 1.329 0.047 1.240 1.424

b 0 -2.145 0.392 -5.46 0.000 -2.914 -1.375 1448.84 1465.14 TOVPSD1 b1 0.167 0.018 9.35 0.000* 0.132 0.202 sd(_cons) 0.434 0.097 0.280 0.674 sd(Residual) 1.217 0.043 1.136 1.304

b 0 1.947 0.144 13.54 0.000 1.6 65 2.228 1496.21 1512.51 CPOVPSD1 b1 -0.009 0.001 -6.26 0.000* -0.011 -0.006 sd(_cons) 0.496 0.107 0.3249 0.756 sd(Residual) 1.274 0.045 1.1885 1.365 Continued

158 Table 3.7 continued

b0 1.947 0.144 13.54 0.000 1.665 2.228 1496.21 1512.51 CPOVPSD1 b1 -0.009 0.001 -6.26 0.000* -0.011 -0.006 sd(_cons) 0.496 0.107 0.3249 0.756 sd(Residual) 1.274 0.045 1.1885 1.365

b 0 1.366 0.120 11.4 0.000 1.131 1.601 1465.95 1482.26 PDIOVPSD1 b1 -0.217 0.027 -8.2 0.000* -0.269 -0.165 sd(_cons) 0.551 0.103 0.383 0.794 sd(Residual) 1.230 0.043 1.148 1.318

b 0 -2.932 0.444 -6.61 0.000 -3.802 -2.062 1437.80 1454.10 TOVPSD b1 0.197 0.020 10.03 0.000* 0.159 0.236 sd(_cons) 0.442 0.097 0.287 0.681 sd(Residual) 1.201 0.042 1.121 1.286

b 0 1.837 0.145 12.7 0.000 1.553 2.121 1509.39 1525.69 CPOVPSD b1 -0.007 0.001 -5.01 0.000* -0.010 -0.004 sd(_cons) 0.498 0.109 0.325 0.764 sd(Residual) 1.294 0.046 1.207 1.386

b 0 1.399 0.119 11.74 0.000 1.165 1.632 1472.47 1488.77 PDIOVPSD b1 -0.203 0.026 -7.72 0.000* -0.254 -0.151 sd(_cons) 0.544 0.103 0.3754 0.788 sd(Residual) 1.241 0.044 1.1583 1.33

b 0 -0.336 0.277 -1.22 0.224 -0.878 0.206 1486.05 1502.35 TPOVPSD1 b1 0.099 0.015 6.75 0.000* 0.071 0.128 sd(_cons) 0.405 0.101 0.248 0.659 sd(Residual) 1.276 0.045 1.191 1.367

b 0 1.386 0.179 7.75 0.000 1.035 1.736 1533.71 1550.02 CPPOVPSD1 b1 0.000 0.001 0.08 0.939 -0.003 0.003 sd(_cons) 0.495 0.111 0.320 0.768 sd(Residual) 1.332 0.047 1.243 1.428

b 0 1.371 0.122 11.28 0.000 1.132 1.609 1464.30 1480.60 PDIPOVPSD1 b1 -0.253 0.030 -8.31 0.000* -0.313 -0.194 sd(_cons) 0.564 0.106 0.391 0.814 sd(Residual) 1.227 0.043 1.145 1.315

b 0 -0.689 0.295 -2.33 0.02 -1.267 -0.110 1476.07 1492.38 TPOVPSD b1 0.112 0.015 7.53 0.000* 0.083 0.141 sd(_cons) 0.403 0.099 0.249 0.653 sd(Residual) 1.261 0.044 1.177 1.351

b 0 1.801 0.168 10.72 0.000 1.472 2.131 1522.99 1539.29 CPPOVPSD b1 -0.004 0.001 -3.31 0.001* -0.007 -0.002 sd(_cons) 0.496 0.109 0.322 0.764 sd(Residual) 1.315 0.046 1.227 1.409

b 0 1.354 0.122 11.12 0.000 1.115 1.593 1460.11 1476.42 PDIPOVPSD b1 -0.250 0.029 -8.61 0.000* -0.306 -0.193 sd(_cons) 0.567 0.105 0.395 0.815 sd(Residual) 1.220 0.043 1.139 1.308

159 Predictor Std. Parameter Estimate z p>|z| 95% CI AIC BIC Indices Err.

b0 -3.690 0.603 -6.12 0.000 -4.872 -2.508 1456.825** 1477.202** TE_H3(x1) b1 0.227 0.025 9.01 0.000* 0.178 0.277 CPE_H3(x2) b2 -0.003 0.002 -1.77 0.078 -0.007 0.000 sd(_cons) 0.455 0.097 0.300 0.689 sd(Residual) 1.210 0.043 1.130 1.297

b0 -2.200 0.554 -3.97 0.000 -3.285 -1.115 1483.831 1504.207 TE_H2(x1) b1 0.164 0.023 7.11 0.000* 0.118 0.209 CPE_H3(x2) b2 -0.004 0.002 -2.08 0.037* -0.007 0.000 sd(_cons) 0.449 0.098 0.293 0.688 sd(Residual) 1.251 0.044 1.167 1.340

b0 -1.727 0.585 -2.95 0.003 -2.873 -0.581 1498.216 1518.593 TE_H1(x1) b1 0.143 0.024 5.88 0.000* 0.095 0.190 CPE_H3(x2) b2 -0.004 0.002 -1.9 0.057 -0.007 0.000 sd(_cons) 0.453 0.100 0.294 0.699 sd(Residual) 1.272 0.045 1.187 1.363

b0 -3.620 0.662 -5.47 0.000 -4.917 -2.324 1470.317 1490.693 TE_H(x1) b1 0.224 0.028 8.08 0.000* 0.169 0.278 CPE_H3(x2) b2 -0.003 0.002 -1.49 0.136 -0.006 0.001 sd(_cons) 0.458 0.097 0.302 0.695 sd(Residual) 1.230 0.043 1.148 1.318

b0 0.037 0.273 0.14 0.892 -0.499 0.572 1445.16 1465.53 TPOVPDSD1(x1) b1 0.077 0.014 5.37 0.000* 0.049 0.104 PDIPOVPSD1(x2) b2 -0.218 0.030 -7.23 0.000* -0.277 -0.159 sd(_cons) 0.503 0.100 0.341 0.741 sd(Residual) 1.195 0.042 1.115 1.280

b0 -1.460 0.399 -3.66 0.000 -2.241 -0.678 1417.06** 1441.51** TOVPSD1(x1) b1 0.142 0.017 8.29 0.000* 0.109 0.176 CPOVPSD1(x2) b2 -0.003 0.001 -2.04 0.041* -0.006 0.000 PDIOVPSD1(x3) b3 -0.149 0.029 -5.14 0.000* -0.206 -0.092 sd(_cons) 0.495 0.094 0.341 0.718 sd(Residual) 1.139 0.040 1.063 1.220

b -2.339 0.454 -5.15 0.000 -3.230 -1.449 1417.84 1442.29 0 TOVPSD(x ) b 0.173 0.019 8.97 0.000* 0.135 0.211 1 1 CPOVPSD(x ) b -0.001 0.001 -0.65 0.513 -0.004 0.002 2 2 PDIOVPSD(x ) b -0.151 0.029 -5.29 0.000* -0.207 -0.095 3 3 sd(_cons) 0.499 0.094 0.344 0.723

sd(Residual) 1.139 0.040 1.063 1.221

b -0.733 0.354 -2.1 0.038 -1.427 -0.040 1439.99** 1464.437** 0 TPOVPSD(x ) b 0.098 0.015 6.64 0.000* 0.069 0.127 1 1 CPPOVPSD(x ) b 0.003 0.001 2.00 0.046* 0.000 0.006 2 2 PDIPOVPSD(x ) b -0.239 0.031 -7.8 0.000* -0.299 -0.179 3 3 sd(_cons) 0.513 0.098 0.354 0.745

sd(Residual) 1.168 0.041 1.090 1.252

b -0.276 0.335 -0.82 0.411 -0.933 0.381 1455.83 1480.29 0 TPOVPDSD1(x ) b 0.082 0.015 5.61 0.000* 0.054 0.111 1 1 CPPOVPSD1(x ) b 0.002 0.001 1.61 0.107 0.000 0.005 2 2 PDIPOVPSD1(x ) b -0.240 0.033 -7.27 0.000* -0.305 -0.176 3 3 sd(_cons) 0.508 0.099 0.347 0.745

sd(Residual) 1.192 0.042 1.112 1.277

Table 3.8. Multivariate regression coefficient estimates

160 3.5 Discussion

The significant association between human WNV onset week and mosquito IRs at every lag period did not support research hypothesis number six, which stated that the two-week lag would be statistically significant in explaining the variance of human case onsets. Since all of the lag periods were significant, the two-week lag period was chosen, which had one of the lowest P values, as the estimate of the most significant lag time between the mosquitoes transmitting the WNV during a human blood meal (the exposure event) and the onset week of the human disease. An estimated two-week lag between the case onset week and the trap week, i.e., the incubation period, indicated that the exposure may have occurred either during the trap week or one week prior to the trap week, which would have placed the exposure during the GON or GON1 index time-delay that represented the gonotrophic period of the mosquito life cycle when blood seeking behavior occurs. The fact that statistical significance was found at every lag period supports the conclusion that was reached in Chapter 2 that the lag periods between mosquito density, IRs, and human case onsets may vary with time and temperature.

The alpha values in the analysis of the association of human WNV case onset and

IRs are overdispersion parameters. The sigma2 values were the variances of the random effects of counties, or county-level random error.

Tables 3.7 and 3.8 document the results of the statistical analysis between IR and the ecological predictor indices. Best fit was determined by the evaluation of the P values, and the lowest AIC/BIC in combination with the coefficient values, β1 - βp, with emphasis on biological relevance of the index and coefficient. For example, the model including TE_H3 and CPE_H3, even with a P value for CPE_H3 of 0.078, had the lowest

161 AIC/BIC values of the models constructed with these two predictor indices, and it was this writer’s judgment to include the index because of the ecological relevance of decreasing CP during the period from eggs to adult emergence, even though the TE_H3 coefficient estimate lost some of its predictive power. The standard deviations of the random effects county level residuals (error) and the fixed effects between-week residuals, sd(_cons) and sd(Residual), respectively, were also considered. The model including TE_H3 and CPE_H3 had the lowest random effects county level estimated standard deviation (0.455) and standard error (0.097) and the lowest fixed effects between-week estimated standard deviation (1.210) and standard error (0.043), compared to the model including TE_H2 and CPE_H3 with two significant predictor indices.

The research hypotheses that were supported by the results of this statistical analysis were that significant increases in IRs were associated with: 1) an increase in T during the OVP index, the GON2, E_H3, OVPSD1, POVPSD, and overwintering time- delayed indices; 2) a decrease in CP during the E_H3 and OVPSD1 time-delayed indices;

3) an increase in CP during the POVPSD time-delayed index; and, 4) a decrease in the

PDI during the OVPSD1 and POVPSD time-delayed indices. Also, an increase in mosquito IR was found to be associated with a significant increase in WNV infection in humans within two weeks after the week of trapping. Figure 3.7 illustrates the temporal location of life cycle and ecological phases associated with these results.

Two hypotheses that were not supported by these findings were: increases in IRs were not significantly associated with a decrease in CP and PDI during the overwintering time-delayed index. An unexpected finding was that an increase in T was a significant driver of IRs during the POVPSD time-delayed index.

162 PDI T CP T

T PDI T CP CP T T

-56 -52 -48 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 T -1 +1

Weeks before trap week (TW) TW

Weaknesses of this study included the non-random nature of the human case sample used in the analysis. It was this writer’s judgement to include in the study only human case onset weeks by county and year that had corresponding mosquito surveillance and calculated IRs. Therefore, these results cannot be interpreted or generalized to the state of Ohio or to any individual county.

Further limitations of this study include the differences in surveillance and control methods used by individual counties, which included mostly non-randomized trap placement, brew mixtures, trapping time frames, the use of larvicides and/or adulticides, public education and other prevention strategies.

These results were statistical in nature and should not be interpreted as causal, i.e.; it would be improper to conclude that increases in T caused IRs to increase or increases in IRs caused human case onsets to increase. The results of a significant association between an index used in this study and IR, or between IR and human WNV case onsets, should be interpreted as a disapproval of the null hypothesis that there was no significant difference between a predictor univariate or covariate and its outcome. The results

163 presented are static and should not be interpreted as a representation of WNV transmission dynamics among humans, bird hosts, and mosquitoes. The significant results of this study will be incorporated into a future dynamic mathematical model to estimate when increased IRs will occur in mosquitoes.

The result of a one unit increase in IR increased the log count of human WNV cases by β1 = 0.05737 (P=0.0001), or a one unit increase in T during the TGON time- delay increased the log count of IR by β1 = 0.0264, was significant, but not predictive.

The use of “predictor” when referring to the indices in this paper was used as a general description of the regression variable (compared to “outcome” variables) on the x axis, and was used in a distinctly different context as indices that were referred to as “drivers” of IR, which implies that they may have been associated with increases in IR, but not predictive of IR increases, and that IR may have been associated with an increase in human WNV disease onsets, but not predictive of these increases.

The scope of this study was ecological in nature due to a county-level data resolution, compared to a resolution at the address-level of a human case, or foci, trap location or street-level to test for the significant drivers of human case onsets and IRs, and hence subject to ecological fallacy. For example, it was not known if the human exposure to the virus occurred in the reported county of onset, or if virus transmission dynamics were affected by trap-level biological and meteorological conditions were lost in the aggregation of data at the county and state level. Therefore, it would be incorrect to make inferences about individual exposures from county-level case onset data. These data were aggregated geographically by county (a county-level resolution) to perform the statistical analyses. Level-1, or the smallest unit of analysis for this study, was the weekly 164 measurements of mosquito IRs or time-delayed indices. Level-2, or the county-level, could be compared to studies of measuring birthweights of children from N mothers, a classic example given for these methods (Rabe-Hesketh and Skrondal, 2012) where mothers (subjects) would compare to counties, and children could compare to weeks.

Six hundred sixty-nine human cases from 2002-2006 were reduced to 339 weeks of data when aggregating human cases by county, year, and week. These 339 weeks were further reduced when pairing human case onset weeks with trap week surveillance data at a county level because 75 of 88 (81%) counties reported mosquito surveillance data and

68 of 88 (77%) counties reported human cases. Because the data were aggregated to analyze the association between weekly mosquito IRs and weekly human case onsets lagged by one, two, three and four weeks, the analyzable weekly data were further reduced as discussed earlier to five datasets from 164-190 weeks, again arranged by county, week, and year.

To create a larger dataset for the statistical analysis, these four datasets were combined, and the only variables that were analyzed were the ecological indices (the predictor variables) and mosquito IR as the outcome variable, creating a dataset of 435 weeks for analysis. The geographically based county-level resolution with a weekly unit of analysis created a multilevel model with mixed effects from the residuals associated with the error between weeks (within counties) and between counties, hence the use of mixed model regression techniques.

The analytical methods introduced bias and deviations from model assumptions into the estimates of coefficients and confidence intervals. Although the results were biologically relevant, the coefficients should be interpreted with caution due to the

165 assumptions that are inherent to the unbiased estimation of linear regression coefficients: lack of multicollinearity, normality of the residuals, and linearity between the response and predictor variables, independent and uncorrelated residuals, and homoscedasticity of residuals. The mixed model, (β0 + ζj) + β1x1ij +… + βpxpij +εij, splits the total residual

(error) into two error components: 1) ζj, the random effect of counties, or the between county residual or random intercept, is the random deviation of the individual counties mean from the overall statewide mean of the ecological indices; and, 2) εij, the random deviation of weekly index values from the county’s mean. The model can be considered a regression model with an added residual, ζj, or a county-specific intercept (β0 + ζj). The model’s random intercept, ζj, is not estimated with the fixed parameters, but is estimated with the variance of εij. ζj is shared between weeks or with the εij of the random deviation of the weekly values, and each week has its own unique εij. These two components account for the assumption that the weekly IRs or meteorological data from the same county are uncorrelated and independent. The specific error is uncorrelated over counties, the week-specific error is uncorrelated over counties and weeks, and the two error components are uncorrelated with each other (Rabe-Hesketh and Skrondal, 2012).

The independent residuals assumption is met according to Rabe-Hesketh and

Skrondal (2012) because the “dependence between the weekly responses is solely due to the shared random intercept. The weekly values “are ‘conditionally independent’ because they are regressed on ζj.” It follows that the weekly values “are conditionally uncorrelated given ζj.”

Normality of the residuals distribution was tested using the STATA “qnorm” command, which plots the quantiles of a variable against the quantiles of a normal 166 distribution, with sensitivity to the tails of the distribution. The residual distribution had a small skewness in the lower tail, which would not put inference at risk (Rabe-Hesketh and Skrondal, 2012). Collinearity, when two (or more) variables are near perfect linear combinations of one another, was tested using the STATA “collin” command for each of the significant covariate models and resulted in variance inflation factors (VIFs) within the acceptable range of 1.00 to 1.24. Linearity of the fixed effects was tested by plotting the log of IR against the residuals of the fitted values and found to be linear.

Homoscedasticity, homogeneity of the variance of the residuals, was tested using a scatter plot of fitted versus residual values for the fixed part of the model only.

Heteroskedasticity was found at the high end of the predictors (Hayes and Cai, 2007), which would affect the interpretation of coefficients. Sample size may appear to be a concern, but since the weeks of data were selected non-randomly, no formula existed to calculate a sample size estimate. Not meeting the assumption of homoskedasticity was the most critical limitation in this study, causing an underestimate of the standard errors, resulting in confidence intervals that were too narrow, P values that were too small, and the potential for Type I errors (Hayes and Cai, 2007). Consequently, the results should not be considered as statistically inferential to individual counties, nor used solely as a basis for control strategies, but merely as additional insight to understanding the drivers of mosquito IRs.

3.6 Conclusion

Mosquito control managers could use the results from this study to further their understanding of the meteorological drivers affecting increases in IRs in their jurisdictions, and the temporal relationship between peak IRs estimated to be two weeks

167 prior to human case onsets. Using a static model to describe a dynamic system between meteorological and biological drivers and IRs will not account for the effect of the relationship of changing ecological conditions, bird and mosquito densities and infection status, and transmission hazards, and should be interpreted with prudence.

Recognizing the limitations of a static model in an attempt to explain increases in

IRs using meteorological conditions and the potential for these increases to affect the risk of WNV disease in humans, the statistical model presented in this study could be used for further insights into the methods and timing of mosquito control efforts.

168

Chapter 4: A practical weather driven model of West Nile virus transmission and human

health risk in Ohio, 2002-2006

4.1 Abstract

A mathematical model structure of differential equations was created with the components of larval mosquito (Lm), susceptible mosquito (Sm), exposed (or infected) mosquito (Em), infectious mosquito (Im), susceptible birds (Sb) and infectious bird (Ib).

The results of the research were insights into the model’s behavior when it was applied and fit to different counties and time frames, when a time variable temperature function replaced the mosquito bite rate parameter constant, when mosquito bite rate and mortality rate parameter constants were modified, and when control strategies were added to reduce WNV transmission hazards and public health risks. The model was calibrated using Ohio and Franklin County, Ohio, 2002, mosquito infection rate (IR) data, and visually fitted to Franklin County, 2003, and Cuyahoga County, 2002 and 2003 IR data.

Temperature was introduced into the model as a time variable bite rate regression function of the effects of temperature on the length of the gonotrophic cycle using a smoothed polynomial curve fit of temperature during the 2002 Franklin County mosquito control season, resulting in the reduction the peak Im density by 30%, increased Em and

Im densities between weeks 28 and 32 by 25%, but did not shift density peaks. To connect the model with real-life mosquito control, combinations of weekly reductions of

169 adult and larvae were added to the model. The most cost-efficient and effective control strategy appeared to be a combination of 10% larvae and 30% adult control. The weekly bite rate parameter was increased from 4.5 per mosquito per week to 5.5 and the mosquito natural mortality rate parameter was decreased from 0.59 per mosquito per week to 0.33, and the results were that peak Em and Im density occurred 5 weeks earlier and increased by a factor of 10, respectively.

The Franklin County, 2002, model was further translated to real-life control strategies by adjusting the mosquito death rate parameter from 0.59 to 0.70 per mosquito per week to attain a R0 <1 and plotting the resulting densities. Control strategies were adjusted to a 12% weekly larval reduction combined with a 5% weekly adult reduction to produce a similar Im density and an estimated R0 < 1.

4.2 Introduction

Mathematical modeling/model (MM) has been used since the 1950’s for many vector-borne infectious diseases to predict the transmission of the agent, the abundance of the vector and host species, and the risk of human infection. MM has been critical to the understanding of transmission dynamics in complex interrelated systems involving vectors (mosquitoes), hosts (birds, including human hosts), and ecological factors that change over space and time. MM contain equations representing these dynamic changes separated into compartments of the transmission cycle. Figure 4.1 is this writer’s modification of the original “epidemiological triad” (Snieszko, 1974), which is a representation of the interrelationship between the agent, vector, and host(s), but adds the ecological component of the environment and its affect on the transmission of risk. The vector, pathogen, and host compartments, interacting with their environment, intersect in

170 a common zone (represented by a triangle) that produces the conditions of disease transmission or increased risk. This relationship is the framework of the classic infectious disease model in epidemiology shown in Figure 4.2 (Centers for Disease Control and

Prevention, 2013b). In Figure 4.2, the diagram on the left depicts the host, agent, and the environment as interdependent and having equal influence on disease outcomes, and in the diagram on the right, the agent and host are dependent upon each other and the environment influences them both equally (Centers for Disease Control and Prevention,

2013b).

Figure 4.1. Vector, pathogen, host, environment relationship

Figure 4.2. The epidemiologic triad of agent, host and the environment 171

In estimating the risk of disease involving the dynamics of transmission of a pathogen using MM, the host, agent, and the environment model in Figure 4.2 also provides the epidemiological foundation used to construct a framework with compartments built with the infection status of vectors and hosts. Vectors and hosts are described as either susceptible (S) or infectious (I) in a S-I model, either susceptible (S), infectious (I) or recovered in a S-I-R model, or susceptible (S), exposed or infected (E), infectious (I), or recovered (R) in a S-E-I-R model. The MM equations written for each compartment are composed of constants, and parameters quantifying the dynamic biological changes in vectors and hosts, which are typically rates or probabilities, such as, but not limited to, birth and death rates, recovery rates, contact rates, and probabilities of transmission, which are derived from the analysis of an empirical dataset or literature values from other field or laboratory studies.

Six weather driven mathematical models of mosquito population dynamics were found in the literature, which used a mosquito life cycle approach as the conceptual framework of the model (Bouden, Moulin, and Gosselin, 2008; Hoshen and Morse, 2004;

Schaeffer, Mondet, and Touseau, 2008; Morin and Comrie, 2010; Gong, DeGaetano, and

Harrington, 2007; Gong, DeGaetano, and Harrington, 2011). These studies modeled mosquito population and transmission dynamics through each development stage of the mosquito life cycle using temperature and precipitation, or proxy precipitation parameters, with the outcome being mosquito density and/or estimates of risk.

Morin and Comrie (2010) created a Dynamic Mosquito Simulation Model

(DyMSiM) with inputs of water availability, land-cover type, climate conditions

172 (temperature and precipitation), habitat suitability, population density, hours of daylight, and land area size to model Culex quinquefasciatus, the southern house mosquito. The model’s approach was based upon the progressive development within mosquito life phases from egg to adult promoted by the environmental conditions in every phase. The model showed agreement with data from traps baited with CO2 resulting in the conclusion that modeled changes in the mosquito population were affected by temperature and precipitation.

Gong et al. (2007) formulated a model with four distinct phases, eggs, larvae, pupae, and adult to predict Culex spp. mosquito abundance. The difference equations used were:

Number of Immature (t) = Number of Immature (t-1) *[Survival Rate of

Immature (t-1)-Development Rate (t-1)] + Number of Adults (t-1) * Egg Laying

Rate (t-1)

Number of Adults = Number of Adults (t-1) * Survival Rate Adults (t-1) +

Number of Immature (t-1) * Development Rate (t-1) * Diapausing Rate

Inputs were daily average temperature, moisture index calculated by summing the daily difference of precipitation and evaporation over the preceding seven days, and hours of the day. Development rate, daily survival rate, and daily egg laying rate were functions embedded in the difference equations. Temperature, as included in the development rate and survival rate functions, represented the average temperature from the 1st of April to

December 31, in each year of the study. The simulated model showed remarkable agreement with actual adult mosquito populations (Gong et. al., 2007).

173 The Multi-Agent GeoSimulation (MAGS) System developed by Dr. Moulin was used to simulate (in a virtual geographic environment) the abundance of crows and Culex spp.(pipiens and restuans) and the dynamic interaction between these populations that were related to abundance and the “evolution of infection” (Bouden et al., 2008).

Meteorological inputs were changes in temperature and degree of humidity resulting from rainfall. Four different scenarios were modeled. First, a scenario was modeled with temperature and rainfall inputs. The second modeled scenario introduced the effect of washouts (a flushing parameter) of mosquito larvae from catch basins from heavy rainfall because the authors hypothesized that too much rainfall caused important oviposition sites, such as catch basins and stagnant ditches, to flush. The third scenario modeled was a simulation of the effects of larvicide use, and the fourth was a combination of scenario two and three. The differential equations used were similar to Wonham, de-Camimo-

Beck, and Lewis (2004), which were the basis for the equations used in this study.

Model parameters were calibrated using various climate and larviciding scenarios to affirm actual mosquito abundance during the study period.

Gong et al. (2011) simulated Culex pipiens and Culex restuans abundance with two discreet models, a degree day (DD) model and a development model, both using a temperature dependent function to calculate the days from egg to adult, and to estimate the percentage of mosquitoes to hatch, respectively. Difference equations were developed for each model to calculate the abundance of eggs laid, adults, and immature (larval stage) on each day. Within these equations were functions developed for development rate from eggs to adult, survival rates for immature and adult life stages, and an egg laying rate using a moisture index calculated from the difference between precipitation

174 and evaporation during the preceding seven days. High correlations were obtained between actual and simulated abundance. Gong et al. (2011) also recognized the importance of flushing to overall mosquito densities.

Schaeffer et al. (2008) simulated “water availability” at a tree hole breeding site for two Aedes species, vectors of yellow fever, resulting in “a good match between simulated populations and field data” (Schaeffer et al.).

Hoshen and Morse (2004) developed a weather-driven, within-vector stages model for malaria transmission by the vector Anopheles gambiae, for both immature and mature population dynamics from egg, larvae to pupae, and the gonotrophic period of searching for a blood meal, egg development (digestion), and oviposition. A 10-day sum of daily precipitation and a 10-day mean of maximum daily temperature offset by 5°C were used. Larval development was found to be a function of temperature and rainfall while adult stages were temperature dependent for two constant temperatures, 28ºC and

19ºC.

This study was an extension of the research found in Chapter 3 that identified statistically significant drivers of mosquito infection and human West Nile virus (WNV) disease risk. Novel time-delayed indices were created that represented phases of the mosquito life cycle and ecology and were temporally related to the week of trapping.

These indices were informed with weekly mean temperature (T), weekly cumulative precipitation (CP), and the weekly Palmer Drought Index (PDI). The following drivers and time frames were significantly associated with increases in mosquito infection rates

(IRs), and could be considered when planning mosquito control efforts: 1) an increase in

T during the oviposition (OVP) index, the gonotrophic (GON2), eggs-to-hatch (E_H3),

175 oviposition and preoviposition site development (OVPSD1 and POVPSD), and overwintering time delay indices; 2) a decrease in CP during the E_H3 and OVPSD1 time-delayed indices; 3) an increase in CP during the POVPSD time-delayed index; and,

4) a decrease in the PDI during the OVPSD1 and POVPSD time-delayed indices.

Since the results from statistical analyses are static, they should not be interpreted as a representation of WNV transmission dynamics among humans, bird hosts, and mosquitoes. Using a static model to describe a dynamic system does not account for the effect that ecological changes over time might have on increases in IRs, plus the cumulative and combined effects over time of other drivers such as precipitation, host competency, and mosquito density. MMs have been applied in research involving WNV, malaria, yellow fever, and a host of other diseases with complex transmission dynamics.

An additional advantage of constructing and applying MMs to complex biological and meteorological systems is that the assumption of independent residuals fundamental to linear regression is relaxed.

This study attempts to answer these specific questions: 1) Can a bird/mosquito

WNV transmission model be developed and calibrated that could predict both sub-apex and apex peaks of WNV transmission in Im from the Ohio, 2002, dataset of IRs? 2) Can the model developed with the Ohio, 2002, IR dataset be fitted to individual counties during 2002 and 2003? 3) Can the model developed provide insights to the influence of mosquito density and mosquito infection on the transmission of WNV hazard? Which one is more important to the practitioner when making decisions to control WNV transmission hazards? 4) Can the model developed give insight on what key control practices or combination of practices that minimize cost and maximize the benefit of

176 hazard reduction? What component of the transmission cycle is control most effective to reduce transmission hazards?

The hypotheses for this study were: 1) A model will be parameterized and calibrated with literature values and significant temperature and precipitation indices of

WNV infection in mosquitoes resulting from answering research question 2, which will predict peak Ohio, 2002, actual mosquito IR; 2) The model developed will be recalibrated by changing parameter values and fit to 2002 and 2003 actual mosquito IR for Franklin and Cuyahoga Counties; 3) Mosquito density and mosquito IR are equally important in the control of WNV transmission hazards; and, 4) Control practices are most effective if applied to the larval model component, and when combined with a 60% reduction in the adult mosquito population, results in the most cost-effective control method

The objectives for the study were that a MM will be developed by integrating functions containing meteorological drivers of WNV disease transmission and literature parameter values into differential equations in order to characterize the biological processes fundamental to increased WNV infections in mosquitoes. The MM will be developed, evaluated, and calibrated at the county level with the same dataset that resulted in the identification of statistically significant drivers in specific aim 2. The evaluation of the model will determine if there is a favorable comparison to the outcomes of WNV infections in mosquitoes. If the match between the predicted and observed outcomes is not favorable, the parameters, functions, or differential equations will be adjusted accordingly (Oreskes, Shrader-Frechette, and Belitz, 1994).

177 The model calibrated in specific aim 3 will be evaluated and re-calibrated using a dataset from the same county and a different year of the study, a different county from the same year, or a county from a year outside of the study time frame with high mosquito and human infection rates. The evaluation of the model will determine if there is a favorable comparison to the outcomes of WNV infections in mosquitoes in the independent dataset. If the match between the predicted and observed outcomes is not favorable, the parameters, functions, or differential equations will be adjusted accordingly (Oreskes et al., 1994). The model will then be connected to real-life disease management strategies by testing the effect of interventions on WNV transmission.

4.3 Methods

4.3.1 Mosquito WNV infection rate data, collection and management

Mosquitoes were collected by local health departments, mosquito control districts, and local communities operating an adult mosquito surveillance program throughout

Ohio using gravid or Centers for Disease Control and Prevention (CDC) light traps according to standard baiting methods. Within each jurisdiction, trap locations were chosen randomly, or by using grid placement methods, foci of transmission based placement methods, or a combination of these methods, in safe locations with permission from property owners, if applicable. Jurisdictions maintained consistent trap locations for the entire mosquito season or changed locations depending on complaints or newly discovered foci of transmission based upon human disease data. Traps were placed overnight (i.e., trap night), and specimens were collected the next day, usually as soon as possible after sunrise. Depending on the jurisdiction, the number of locations varied from one to over 30 each trap night. The number of trap nights per week varied among

178 jurisdictions, with the one or two trap nights being the norm and three per week the exception, because of the intensity of the labor necessary and subsequent costs incurred with each collection. Once collected, specimens from each trap were frozen, and treated as independent samples. Specimens were then counted, and males and females were separated, Culex spp. were identified and placed in pools of 50 or fewer individuals, and the remaining species were pooled accordingly. Each pool was identified by trap number and shipped or driven to the Vector-Borne Disease Program (VBDP) laboratory for real- time reverse transcription polymerase chain reaction (RT-PCR) testing for the WNV per the standard methods of RNA extraction found in Mans et al. (2004). Positive and negative pool results were recorded by lab technicians in an electronic spreadsheet. All positive results were given to the local jurisdiction by phone or e-mail message as soon as the test was completed. Each month, an updated arbovirus report in the form of the spreadsheet was sent by e-mail to mosquito control managers (Ohio Department of

Health, 2005).

Cycle threshold (CT) values were recorded by hand in a hard-copy lab notebook per mosquito pool number for years 2003 to 2006 and matched with the unique pool number. Records for 2002 could not be found in storage. The parameter CT is defined as the cycle number required for dye fluorescence to become higher than background fluorescence level. The method is based on the inverse exponential relationship that exists between the initial quantity (copy number) of target sequence copies in the reactions, and corresponding CT determinations, i.e., the higher the starting copy number of the RNA target sequence, the lower the CT value (Mackay, Arden, and Nitsche, 2002).

Any sample with less than a threshold cycle value of 35 was considered positive.

179 Mosquito infection data for the years 2002 to 2006 were accessed by direct written request to personnel in VBDP managing Ohio’s historical arbovirus database.

The data were contained in a spreadsheet in which each row contained a unique pool identification number assigned by the lab technician who performed the real-time RT-

PCR test. The columns on the spreadsheet included data for the collection date and a trap identification number assigned by the surveillance agency, the species collected identified by VBDP lab personnel, the number of mosquitoes in each pool determined by the surveillance agency, the county of collection, the surveillance agency that submitted the specimens for testing, real-time RT-PCR results (+/- for WNV), date tested, and year tested, all recorded by lab personnel.

Mosquitoes were submitted from 75 of the 88 (81%) counties in Ohio from 2002 to 2006. Table 4.1 contains the mosquitoes of all species that tested positive for WNV

(CDC, 2013; Ohio Department of Health, 2010). Table 4.2 is a statewide summary of the number of pools tested, the number positive, the density, and the statewide aggregated minimum infection rate (MIR) for only Culex spp. during the years 2002 to 2006, showing an approximate three to five-fold decrease in MIR between 2002 and 2003 to

2006.

180

Mosquitoes Pools Year Tested # Pools + 2001 91,590 No data 26 2002 187,046 8,246 2082 2003 490,249 19,796 799 2004 398,832 14,202 874 2005 390,010 14,705 1373 2006 444,074 15,353 913 Total 2,001,801 72,302 6,067 Table 4.1. Total of all mosquito species tested, number of pools

tested, and positive pools by year for Ohio, 2001-2006

Before IRs were calculated, all mosquito samples and pools caught from light traps were removed from the database to facilitate an analysis of only mosquitoes caught in gravid traps, with the exception of the Lake County light traps. The database was also stripped of all mosquito species except Culex spp. or Culex pipiens, to ensure an analysis that is not biased by the breeding or feeding behaviors of other mosquito species. IRs were calculated by two methods: 1) MIR, the number of positive mosquito pools in each sample (one sample per trap and one or more pools per sample) divided by the total

# # Pos Year Pools pools Density MIR 2002 5669 1752 174652 10.0 2003 10840 724 361787 2.0 2004 10839 837 375304 2.2 2005 10769 1327 375799 3.5 2006 11569 896 420724 2.1 Total 49686 5536 1708266 3.2

Table 4.2. Number of Culex spp. pools tested, number of positive pools, number (density) of Culex spp. collected, and aggregated minimum infection rate (MIR) of Culex spp. for

Ohio by year, 2002-2006

181

number of mosquitoes tested times 1000, 2) calculating the maximum likelihood estimate

(MLE) for each sample (one sample per trap and one or more pools per sample). The

MLE is an estimate of the actual number of positive mosquitoes in a sample times 1000

(using PooledInfRate software as an add-on to Excel, Biggerstaff, 2003). The sample

MIR was used if every pool per sample was positive, which renders the differential equations used in the calculation of the MLE unsolvable.

After calculating the sample IRs, it was apparent that there were extreme outliers in the range of IRs for each sample, county, and week. These outliers were caused by very small sample sizes, such as one positive pool and a sample size of one, which calculated to an estimated MIR of 1000; one positive pool and a sample size of two calculated to an estimated MIR of 500, one positive pool and a sample size of four calculated to an estimated MIR of 250/1000; and two positive pools with a sample size of five calculated to an estimated MIR of 400/1000. Placing this phenomenon into perspective, for the year 2002, the state average MIR was 10/1000 for all counties. In some counties, the average MIR ranged from 30 to 50/1000 during peak transmission.

To achieve an 80% probability of detection during peak transmission periods, sample sizes of 31 to 52 were found to be necessary (Gu and Novak, 2004). For the year 2002,

88 samples of size one to four mosquitoes out of 779 total pools (11%) were positive. In

2006, with a statewide MIR of 2/1000, only four samples of size one to four mosquitoes out of 793 (0.5%) were positive. Because extremely high sample MIRs upwardly biased weekly mean IRs, the estimates of MIRs greater than 200/1000 for every county, week, month, and year were removed. Even though many estimates of MIRs were above the

182 upper fence, the box plots in Figure 4.3 indicated a distinct separation of points above a

MIR of 200/1000. After the removal of any significant outliers, IRs were sorted and

average by county, year, and week, and by week and year for further analysis.

Minimum Infection Rate Outliers for Selected Ohio Counties, 2002

1,000

800

600

400

Mimimum infection infection rate Mimimum

200 0

Butler County Cuyahoga County

Franklin County Hamilton County

Lake County Lorain County

Summit County Lucas County

Montgomery County Stark County

Figure 4.3. Minimum infection rate statistical outliers for selected Ohio counties, 2002

Figure 4.4 illustrates the mosquito density (actual average density divided by 10),

IR, and weekly human case onsets for Ohio for 2002 and 2003. IR peaked during weeks

34 and 37 in 2002 and during weeks 34, 37, and 39 in 2003.

183 Density, Infection Rate, and Human Cases, Ohio, 2002-2003

120

100 Density(Density/10) 80 MLEIR 60 Human Cases

Number of human human cases of Number 20

3.4 2.2 2.3 2.4 3.2 3.3

Density: Number of mosquitoes mosquitoes week/10trap perNumber of Density: Year.Week Number

IR IR

Figure 4.4. Average mosquito density (actual average density divided by 10) and average infection rate (IR) by trap week, and human cases by onset week for Ohio, 2002 and

2003, plus expanded view

4.3.2. Live and dead bird host surveillance data

Figures 4.5 A to 4.5 G show the exponential decline in the Audubon Society’s

Christmas Bird Count (CBC) estimated crow population from 2001 to 2002, when the epizootic occurred in Ohio. The black (crows) and blue (blue jays) estimated Excel trend lines on Figures A to F are subject to the recognized biases found in the count data from inconsistent Num/Part ratios between count years. Statewide, as seen in Figure 4.5 A, the

CBC crow population rebounded in 2006 to above 2001 count levels. In individual 184

Figure 4.5. Audubon Society Christmas bird count (CBC) crow and Blue Jay density by year for Ohio (A), Cuyahoga County (B), Franklin County (C), Cincinnati and Hamilton

County (D) Lorain County (E), Lucus County (F), and Montgomery County (G) with blue trend lines for Blue Jays and black trend lines for crows, 2001-2006

counties, subjected to the biases of individual count, CBC crow repopulation varied, with the Franklin County population rebounding in 2005 and 2006, the Cuyahoga, Hamilton,

Montgomery County populations never rebounding from 2002-2006, while the Lorain and possibly Lucas County populations experienced a moderate repopulation. The CBC

Blue Jay population, statewide, recovered to meet or exceed pre-epizootic levels.

Table 4.3 is a summary of the number of WNV positive bird deaths by two bird species and “other” birds per year. Besides crows and Blue Jays, 39 other bird species that tested positive. 13% of the species other than crows and Blue Jays were robins, 10% were grackles, 22% were House Sparrows, and 13% were Northern Cardinals. 185 Figure 4.6 A shows the cumulative peak onset (collection) week for WNV dead birds in Ohio from 2002 to 2006. Figure 4.6 B is a graphic illustration of Table 4.4 and shows that, after crows and Blue Jays had declined from 2002 to 2003, other birds became the host species to WNV.

Species 2002 2003 2004 2005 2006 Total American Crow 319 39 18 25 30 431 Blue Jay 481 56 26 17 49 629 Other 30 125 36 31 46 268 Total 830 220 80 73 125 1328 Table 4.3. Number of WNV positive American Crows, Blue Jays, and other species by year, Ohio, 2002-2006

Figures 4.6 C and 4.6 D exhibits the counties that contributed to the peaks in WNV positive birds seen on Figure 4.15 A at weeks 28, 31, and 32.

4.3.3 Meteorological data, collection and management

Daily mean temperature and total daily precipitation data were downloaded from archived sources from the National Oceanic and Atmospheric Administration (NOAA),

National Climatic Data Center, and Land Based Data Sets from 20 primary weather stations located throughout Ohio (NOAA, 2012), and converted to T (ºC) or CP (mm).

186 A Cumulative WNV Positive Birds, Ohio, 2002- B 2006 Arbovirus Host Species, 2002-2006 300 600

250 500

200 400 Crow

150 300 Blue Jay Count Count Other 100 200

50 100

0 0

20 35 21 22 23 24 25 26 27 28 29 30 31 32 33 34 36 37 38 42 19 2002 2003 2004 2005 2006 Week Number Year

C D WNV Positive Birds, Select Ohio Counties, 2002 WNV Positive Birds, Select Ohio Counties, 2002 20 30 18 Allen County 16 Cuyahoga County 25 Green County 14 Franklin County 20 Licking County 12 Hamilton County Madison County

10 Lake County 15 Count Count Medina County 8 Lorain County Mercer County 6 Lucas County 10 Ottawa County 4 Montgomery County 5 Portage County 2 Stark County 0 Wood County 0 21 22 23 24 25 26 27 28 29 30 31 32 33 34 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Week Number Week Number

Figure 4.6. Cumulative number of WNV positive birds by week for Ohio, 2002-2006 (A), number of WNV positive birds by species and year for Ohio, 2002-2006 (B), number of

WNV positive birds by week for Cuyahoga, Franklin, Hamilton, Lake, Lorain, Lucas,

Montgomery, and Stark Counties, 2002 (C), and the number of WNV positive birds by week for Allen, Green Licking, Madison, Medina, Mercer, Ottawa, Portage, and Wood

Counties, 2002 (D)

Each county in Ohio was assigned to its closest weather station based upon distance and prevailing weather patterns in collaboration with a Columbus-based meteorologist (e- mail by Jym Ganahl, Chief Meteorologist, Channel 4, Columbus, Ohio, January 25,

2013).

187 Daily day length data were retrieved from: http://www.timeanddate.com/worldclock/astronomy.html?month=8&year=2013&obj=su n&afl=-11&day=1&n=414, and converted to DL (minutes) from Canton for the eastern section of the state, from Columbus for the central portion of the state, and from

Cincinnati for the western portion. Palmer Drought Severity Index (PDI) data were downloaded from NOAA U.S. climate monitoring weekly products (NOAA, 2012a) for each of the 10 Ohio weather districts.

Table 4.4 contains the estimated time-delays of the indices which were constructed as time periods relative to the week mosquitoes were trapped (weeks before and during the trapping week). Specifically, these time periods estimated the temporal position of phases of the mosquito life cycle, and the ecological conditions necessary for development within these phases, in relation to the trapping week. These indices were oviposition, the egg, larvae and pupae stage (i.e., eggs-to-hatch), the adult stage (which included the gonotrophic cycle and the extrinsic incubation period), two theorized time periods that were needed for optimum oviposition site development, and an overwintering time period from December to March. These indices were informed by T and CP data, and the PDI. Multiple time periods were established for some indices to evaluate which ones may have had a statistically significant association with mosquito and human WNV infections. A "-" symbol preceding the week number indicated a week prior to the trapping week (i.e., backwards in time) and a "+" symbol preceding the week number indicated the week of trapping that was the same week as the estimated oviposition index. For example, to inform the eggs-to-hatch (E_H) indices E_H, E_H1,

E_H2 and E_H3 with T, T data from weeks -2 and -3 were added and the sum was

188 divided by 2, T for week -2 was used, T for week -3 was used, and T data from weeks -3 and -4 were added and the sum was divided by 2, respectively. To inform the gonotrophic

(GON) indices GON, GON1, and GON2 with CP, CP data from weeks +1 and -1 were summed, CP from week -1 was used, and CP data from weeks -1 and -2 were summed, respectively. The 7-day PDI was defined by NOAA as follows:

Index Time- Index code Index Description Delay OVP Oviposition Week +1 GON Gonotrophic Cycle Week +1 : -1 GON1 Gonotrophic Cycle1 Week -1 GON2 Gonotrophic Cycle2 Week -1 : -2 E_H Eggs-to-Hatch Week -2 : -3 E_H1 Eggs-to-Hatch1 Week -2 E_H2 Eggs-to-Hatch2 Week -3 E_H3 Eggs-to-Hatch3 Week -3 : -4 OVPSD Oviposition Site Development Week -5 : -7 OVPSD1 Oviposition Site Development1 Week -6 : -8 POVPSD Preoviposition Site Development Week -8 : -11 POVPSD1 Preoviposition Site Week -9 : -12 Development1 OW December to March (Overwinter) December, January, February Table 4.4. Estimated time-delays of the indices representing the mosquito life cycle and ecology which were constructed as time periods relative to the week mosquitoes were trapped (week +1 is the trapping week)

189 Figure 4.7 is a graphical illustration of the estimated time-delays of the indices relative to the week mosquitoes were trapped. The week of trapping (TW) is indicated by week +1. The TW was assumed to be the week of oviposition. Human exposure to an infectious mosquito was assumed to have occurred during the GON time-delayed indices between weeks -2 to +1.

E-H2 E -H1 GON

POVPSD1 OVPSD1 E-H GON1

OW POVPSD OVPSD E-H3 GON2 OVP -56 -52 -48 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 +1 Weeks before trap week (TW) TW Oviposition (OVP) estimated time period Gonotrophic (GON) estimated time-delay Eggs-to-hatch (E-H) estimated time-delay Oviposition site development (OVPSD) estimated time-delay Preoviposition site development (POVPSD) estimated time-delay

Overwinter (OW) estimated time-delay

4.3.4 Analytical methods

Temperatures in Figure 4.8 (Weeks 0 to 20 represents epidemiological weeks 20 to 40) informed the statistically significant TGON2 index which was introduced within

190 functions into the differential equations used to model larval mosquitoes (Lm) and susceptible, exposed, and infectious adult mosquitoes (Sm, Em, Im) and susceptible and infectious birds (Sb, Ib). The parameters used in the differential equations were from

WNV literature sources. The framework of the model in this study follows the models in

Wonham et al. (2004) and Wohman and Lewis (2008) and is illustrated in Figure 4.9.

Figure 4.8. Mean weekly temperature for the gonotrophic (GON) index, Franklin County,

Ohio, 2002. Weeks 0 to 20 represents epidemiological weeks 20 to 40

191 Eggs Larvae, μω Pupae

SM μω λ μ IB γb1 EM μω λ σ μω

IM SB γb2

Gravid Trap

This analysis attempted to incorporate temperature drivers of WNV infection hazards in mosquitoes and birds into a basic S-E-I model. R software, version 3.03, and Matlab

Student Edition R2014a were used for the modeling in this research. The model components with no temperature influence were as follows: 192

The model components which included the influence of temperature follow:

The following model structure was used for the inclusion of weekly control practices:

193

Table 4.5 includes the parameter definitions and values, and initial densities of the model that was calibrated using the Franklin County, Ohio, 2002 mosquito infection rate and TGON2 time-delayed temperature index. The references cited provided mean values from the literature and were used to guide the process of calibrating the model to local conditions. All units were per capita per week except probabilities, which were unitless.

Bites rates within a frequency dependent transmission model were constant across all levels of bird density and was contolled by the maximum biological biting rate of the mosquito, which if regulated by the gonotrophic cycle and temperature.

194 Model Mean (Range) Literature Parameter Description Unit Parameter Values λ=birth rate mosquitoes with larval class 2.35 3.76(0.252-292.5)* Per capita/week ml=maturation % of larvae surviving to 0.35 0.49(0.35-0.63)** Per capita/week adults mμm= natural death rate mosquitoes 0.59 0.21(0.14-0.49)** Per capita/week γ=bite rate 4.50 3.08(2.38-3.71)**% Per capita/week σ=virus incubation rate 0.59 0.70(0.63-0.84)** Per capita/week b1=transmission probability b to m 0.45 0.69(0.23-1.00)** Unitless b2= transmission probability m to b 1.00 0.74 (0.27-1.00)** Unitless mμb= death rate birds from virus 0.86 1.00(0.875-1.4)* Per capita/week mμl=natural death rate larvae 0.88 0.14(0.07-0.36)** Per capita/week ω= adult and larval death due to Percentage man-made control methods death /control week Model Functions γ(T)=bite rate regression of T 0.28 + 1/(-0.064 + 0.013 x temp))/7# Polynomial curve fit function of temp = -0.0009851*(t+20)^3 + temperature 0.03739*(t+20)^2 + 0.6454*(t+20) -3.211 Model Components Initial Densities Lm 0.9 x 1e4 Sm 0.09 x 1e4 Em 0.01 x 1e4 Im 0.0 Sb 0.95 x 1e3 Ib 0.0 Table 4.5. Model parameter descriptions, parameter values, mean and range literature parameter values, parameter units, model functions, and initial densities of each model component *Wonham et al. (2004) **Wonham and Lewis (2008) # Reisen, Milby, Presser, and Hardy (1992) %Bite rate under frequency dependence

Figure 4.10 plots the bite rate gamma, γ, as a function of temperature using temperature data from the Franklin County, Ohio, 2002, TGON index as shown in Figure

4.8. Epidemiological week 32 corresponded to week 12 on these plots. Figure 4.11 plots the fitted curve using the 3rd degree polynomial in Table 4.5, of the 20 weeks of temperature data from the TGON2 timedelayed index shown in Figure 4.10.

195

Figure 4.10. Bite rate as a function of temperature from the Franklin County, Ohio, 2002,

TGON index. Weeks 0 to 20 represents epidemiological weeks 20 to 40

Figure 4.11. Fitted bite rate curve as a function of temperature from the Franklin County,

Ohio, 2002, TGON index. Weeks 0 to 20 represents epidemiological weeks 20 to 40

196 4.4 Results

Figures 4.12 to 4.33 represent plots of the results of the differential equations, estimated density of Lm, Sm, Em, Im, Sb, and Ib on the y-axis, with weeks on the x-axis.

Week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40. Figure 4.12 for the Ohio, 2002, WNV transmission season the parameters in Table 4.5 were used without the introduction of a weather function into the model. The initial densities were set with a surveillance area reference scale of 0.8km2.

Figure 4.13 contains plots of the same model components with the weather function added. The peaks and sub-peaks (see ovals) of mosquito IRs in Figures 4.14 to 4.17 were analyzed against peaks and sub-peaks of the density of Im and Em because mosquito density was a common factor in both calculations. The units for Im and Em were density, and IRs were unitless in these Figures. The comparison between Im, Em, and IRs was solely based upon the temporal location of peaks and sub-peaks. Figure 4.14 displays the

20 week seasonal model (without temperature added) calibrated to actual mosquito infection rates from the Franklin County, Ohio, for the 2002 mosquito season by changing the gamma parameter to 4.75 per capita/week. Figures 4.15, 4.16, and 4.17 are visually fitted models for Franklin County, 2003, Cuyahoga County, 2002 and Cuyahoga

County, 2003. The Franklin County, 2003, transmission model was calibrated and visually fitted using the same temperature model shown in Figure 4.13 with the gamma parameter changed to 4.75 per capita/week. The Cuyahoga County, 2002, transmission model was calibrated and visually fitted using the same model that was fitted for the

Franklin County, 2002, WNV transmission cycle. The Cuyahoga County, 2003,

197 transmission model was calibrated and visually fitted using the same model as shown in

Figure 4.12.

40.

198

199 Franklin County, 2002

Figure 4.14. WNV transmission fitted model of Franklin County, Ohio, 2002, to actual mosquito infection rate (IR), using parameters in Table 4.5, except the gamma parameter was changed to 4.75 per capita/week, with mosquito IR represented by ▀. IR is not to scale with model estimated densities and used to illustrate peaks only. Time (x-axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40.

200 Franklin County 2003, With Bite Rate Function of Temperature and Time

MLE

Figure 4.15. WNV transmission fitted model of Franklin County, Ohio, 2003, to actual mosquito infection rate (IR), using parameters in Table 4.5, except the gamma parameter was changed to 4.75 per capita/week, with temperature and time variable bite rate function, with IR represented by ▀. IR is not to scale with model estimated densities and used to illustrate peaks only. Time (x-axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40.

201 Cuyahoga County, 2002

IR is not to scale with model estimated densities and used to illustrate peaks only. Time

(x-axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40.

202 Cuyahoga County, 2003

Figure 4.17. WNV transmission fitted model of Cuyahoga County, Ohio, 2003, to actual mosquito infection rate (IR), using parameters in Table 4.5, with IR represented by ▀. IR is not to scale with model estimated densities and used to illustrate peaks only. Time (x- axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40.

To connect the model with real-life mosquito control, combinations of weekly reductions of adult and larvae were added to the model. Figures 4.18 to 4.21 document various combinations of control strategies to reduce WNV hazards from 8-10% larval reduction and 15%, 20%, 30% and 40 % adult mosquito reductions for Ohio, 2002 using the parameters and initial densities in Table 4.5. 203

204

Figure 4.19. Control strategy using larval 10% and adult 20% weekly reduction. Time

(x-axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Ohio, 2002.

205

206

Figures 4.22 to 4.25 simulate a mosquito control strategy which relies on only larviciding for Ohio, 2002.

207

208

209

210

Figures 4.26 to 4.30 simulate a mosquito control strategy which relies soley on adulticiding for Ohio, 2002.

211

212

213

214

215

4.5 Discussion

The following research questions were answered as a result of this study: 1)

Can a bird/mosquito WNV transmission model be developed and calibrated that could predict peaks of WNV transmission in Im from the Ohio, 2002, dataset of IRs? 2) Can the model developed with the Ohio, 2002, IR dataset be fitted to individual counties during

2002 and 2003? 3) Can the model developed provide insights to the influence of mosquito density and mosquito infection on the transmission of WNV hazard? Which one is more important to the practitioner when making decisions to to control WNV

216 transmission hazards? 4) Can the model developed give insight on what key control practices or combination of practices that minimize cost and maximize the benefit of hazard reduction? What component of the transmission cycle is control most effective to reduce transmission hazards?

The model provided estimated Em and Im density peaks and subpeaks that were temporally similar to the actual peak mosquito infection conditions found in Franklin

County, Ohio during 2002 and 2003. These similarities were determined by visual comparison. Franklin County’s mosquito IR sub-peaked at weeks 31 and33 and peaked at week 36 in 2002 and the model plots in Figure 4.14 illustrated temporally similar peaks and sub-peaks for Em and Im occurring between weeks 30 and 36. Franklin County’s mosquito IR sub-peaked at weeks 33 and 35 and peaked at week 37 in 2003 and the model plots in Figure 4.15 illustrated approximately temporally similar peaks and sub- peaks for Em and Im occurring between weeks 32 and 37. The accuracy of the model peaks can be further justified because RT-PCR methods will identify both Em and Im as positive for WNV.

The x-axis in Figure 4.12 represents time in weeks with week 10 equal to epidemiological week 30 and week 15 equal to week 35. The models were all seasonal predictions starting with week 20 (x=0) to week 40 (x=20). Model predictions assumed densely populated urban/suburban conditions with infrastructure > 30 years old, such as what was found in the metropolitan areas of Cleveland, Columbus, Cincinnati, and

Toledo, including older Townships. The model did not include any bird immigration or new birds from births. The initial density of Sb was assumed to be any species of bird and transmission probabilities were not species specific for birds.

217 The parameters in bold in Table 4.5 that were used to create the simulation model in Figure 4.12 were outside of the range of published literature values. For example, a bite rate of 4.5 per mosquito per week may not be biologically possible because that would mean a female mosquito’s egg-laying cycle would be 1.55 days. According to

Bustamante and Lord (2010), the gonotrophic cycle of Culex spp. could be as low as 2 days at temperatures ranging around 25°C. Practitioners examining this model should be aware that the literature bite rate was manipulated to calibrate the model to Ohio and the

Franklin County mosquito IR. Figure 4.31 illustrates the bite rate parameter manipulated to 5.5 per mosquito per week (a gonotrophic cycle of 1.27 days), which shifted the Im peak approximately five weeks earlier in the season because the infection was being amplified throughout the transmission cycle at a faster rate of change.

Other estimated parameters that were of concern included the natural death rate of mosquitoes (0.59 per capita/week) and the natural death rate of larvae (0.88 per capita/week), which are both outside of literature ranges. A death rate of 0.59 per mosquito per week corresponds to a lifespan of 1/0.59, which equals 1.69 weeks or about

12 days. The average lifespan of Culex spp. is two to three weeks (Cruz-Pacheco, Esteva,

Montaño-Hirose, and Vargas, 2005). The 12 day lifespan may have been more realistic in metropolitan counties where adult mosquito control methods were applied consistently in the urban, suburban and rural areas, such as the applications in Franklin County.

Increasing the mosquito lifespan in the Ohio model to approximately three weeks (0.33 per week per mosquito) resulted in a 10-fold increase in the density of Em and Im, shifting the peak Im farther to the left, i.e., depicting a shift in the peak transmission hazards developing at a faster rate of change five weeks earlier in the season

218

(Figure 4.32), compared to the model based upon the parameters in Table 4.5 and in

Figure 4.12. The lifespan of larvae in the model is 1/0.88 which equals about eight days.

Larval lifespan has been reported to be between 8 to 14 days (Gad, Feinsod, Soliman, el

Said, 1989) at 27°C. This lifespan is well within the temperature parameters of the 2002 mosquito season (Figure 4.8).

219

Because of the parameter assumptions, the model should be considered only as an insight into the complex cycle of WNV transmission. Bite rates and transmission probabilities are difficult to accurately predict because, even though they may have been documented under controlled conditions, the ecology of each area being surveyed for the presence of WNV has different transmission dynamics. For example, the transmission probability of mosquitoes to birds equal to 1.00 (which means that the virus will be transmitted 100% of the time) used in the calibration of the model to Ohio and Franklin

County may not be a realistic assumption for other areas. 220 Integrating actual Ohio, 2002, T from the TGON2 time-delayed index shown in

Figure 4.13 (which encompassed the gonotrophic biting and egg development cycle) as a function to replace the parameter constant for biting rate, increased densities between weeks 28 and 32 by 25% and decreased the peak infection density by approximately 30% at week 37, compared to Figures 4.12, but did not shift sub-peaks or peak Em and Im densities.

The simulated adult control strategies were based upon weekly adulticiding using an ultra low volume (ULV) sprayer not exceeding label application rates when WNV was documented in the mosquito population resulting from surveillance. This control method assumes targeted adulticiding within a 0.8 km radius of the trapping location where the positive WNV mosquito was found, because the flight distance of Culex spp. is up to 2 km, but usually stays within 200 meters of the adult emergence location (Trawinski and

Mackay, 2010). Weekly adult mosquito control was simulated at various reduction strategies with and without other control methods.

The simulated larval control method assumed primarily a monthly catch basin control program, using at least a 30 day release product or more frequent treatment if necessary due to washouts, implementation of container and oviposition site reduction strategies, and weekly larval control of standing water conducive to Culex spp. oviposition, such as stagnant ditches and woodland pools. Due to the nature of the model, weekly reduction strategies were used for simulations, which may not reflect actual field practices in some counties.

The parameters estimated for the model in Figure 4.14 fit to Franklin County,

2002, mosquito IR data, produced a estimated mosquito density of approximately 3000

221 Sm, 600 Em, and 550 Im per week during the peak IR at week 33, totaling 4,150 mosquitoes, and at week 31, 2500 Sm, 450 Em, and 400 Im, totaling 3,350 mosquitoes, given the initial estimated densities and parameters in Table 4.5, with the exception of the gamma parameter, which was changed to 4.75 per capita/week. The true densities of Sm,

Em, and Im remained unknown. The actual density of mosquitoes (which include Sm,

Em, and Im) trapped at peak IR week 33 was approximately 412 with an IR of 48/1000, and the actual density of mosquitoes trapped at week 31 was 636 with an IR of 22/1000.

The conclusion reached in Chapter 2 of this dissertation was that mosquito density increases appeared to be directly related to mosquito IR increases, with a two-week lag period, which would have meant that the density at week 31 may have affected the IR at week 33, and consequently human case onsets during week 35.

The IR of 48/1000 during week 33 should be interpreted with discretion because it was inflated due to low pool sizes (mean = 12, n=33). Week 33 had one sample from a trap with one mosquito pool, with a calculated MIR of 200, because a sample of five mosquitos (making up the one pool) tested positive. The mosquito population during week 33 was lower than week 31 possibly due to effective weekly adulticiding and larviciding in areas of positive mosquitoes by both the Columbus City and Franklin

County health departments or sampling errors as discussed in Chapter 1.

Figure 4.33 illustrates Franklin County, 2002, surveillance data for mosquito IR and density (actual density divided by 100), adulticiding weeks, and human case onset weeks. The results from Chapter 2 showed that the density sub-peak at week 31 may have contributed to the peak of IRs at week 33, and this IR peak may have been responsible for the one human case during week 34 and three cases during week 35; the density sub-peak

222 at week 33 may have contributed to the increasing IR at week 35, and this IR increase may have been responsible for the one human case during week 36; the density sub-peak at week 35 may have contributed to the increasing IR at week 36, and this IR increase may have been responsible for the two human cases during week 37; the density level at week 36 may have simultaneously contributed to the IR peak at week 36, and this IR may have been responsible for the one human case during week 40.

Additional insights on the effects of the reduction of density on mosquito IRs can be gleaned from the results of the mosquito control models in Figures 4.18 to 4.21.

The theoretical methods described in Bowman, Gumel, van den Driessche, Wu and Zhu

(2005) and illustrated in Figure 2 of their publication, show how the reduction of mosquito density through various contol strategies effects the objective of keeping the reproductive number (R0), the number of secondary infections caused by one infected individual in a completely susceptible population, below a critical threshold, R0 < 1. It was suggested that a control program that reduces the adult mosquito lifespan to 6 days or a weekly death rate of 1.18 per capita would be sufficient enough to prevent a human outbreak. A larviciding program that could reduce mosquito births to 4000 per day would be sufficient to “eradicate the disease regardless of the presence or absence of other

223 Figure 4.33. Mosquito density (actual density divided by 100) and average infection rate by trap week, adulticiding by control week, and human cases by onset week for Franklin

County, 2002

control measures” (Bowman et al., 2005). As suggested by Wonham and Lewis (2008), the reduction of R0 can be accomplished by the reduction of mosquito density, which, theoretically, should reduce WNV outbreaks.

Finding a definition of an outbreak to apply to this research presented challenges.

One definition found in Campbell-lendrum (2005) was that an outbreak is “where reported disease cases exceeds a threshold of 1.96 multiplied by the standard deviation of the mean for at least two weeks.” Campbell-lendrum (2005) also reported an outbreak defined as “an epidemic limited to localized increase of the incidence of a disease, e.g., a 224 town, village, or closed institution.” For the study of Franklin County 2002 vector-borne disease, the definition of a food-borne outbreak was adapted: two or more cases associated with a common exposure (ideally within a radius of 2 km from a trapping location with positive WNV mosquitoes identified).

The control models developed in this study compared three main strategies: 1) using a combination of larval and adult control, 2) using adult control only, and 3) using larval control only. From a practical standpoint, adult control is more costly than larval control, with high investments in spraying equipment, chemicals, safety training, transportation, liability, supervision, public relations, and labor. Quantifying the per unit cost of reducing hazards by measuring the outcomes of density or mosquito infection is not within the scope of this study. Typically, per unit costs would be higher for adulticiding. Given an estimated and generalized ratio of a $1 cost for larviciding for every $5 spent on adulticiding for each % reduction of density, and outcomes of

10%/30%, 0%/50%, or 50%/0% reductions for larval/adult control, program costs would be $160 for an outcome of 0-1 infected mosquitoes at week 30, $250 for an outcome of 0-

1 infected mosquitoes at week 30, and $50 for an outcome of 25 infected mosquitoes at week 30, respectively. The most cost-effective and efficient hazard reduction strategy appeared to be the combination of 10% larvae and 30% adult control measured by the reduction of adult Em and Im density. The CDC 4th edition of its guidance for WNV prevention, surveillance and control (CDC, 2013c) states:

225 Mosquito Control Activities

“Guided by the surveillance elements of the program, integrated efforts to control mosquitoes are implemented to maintain vector populations below thresholds that would facilitate WNV amplification and increase human risk. Failing that, efforts to reduce the abundance of WNV-infected biting adult mosquitoes must be quickly implemented to prevent risk levels from increasing to the point of a human disease outbreak. Properly implemented, a program monitoring mosquito abundance and WNV activities in the vector mosquito population will provide a warning of when risk levels are increasing.

Larval Mosquito Control

The objective of the larval mosquito control component of an IVM program is to manage mosquito populations before they emerge as adults. This can be an efficient method of managing mosquito populations if the mosquito breeding sites are accessible. However, larval control may not attain the levels of mosquito population reduction needed to maintain WNV risk at low levels, and must be accompanied by measures to control the adult mosquito populations as well. In outbreak situations, larval control complements adult mosquito control measures by preventing new vector mosquitoes from being produced. However, larval control alone is not able to stop WNV outbreaks once virus amplification has reached levels causing human infections.

Adult Mosquito Control

Source reduction and larvicide treatments may be inadequate to maintain vector populations at levels sufficiently low to limit virus amplification. The objective of the adult mosquito control component of an IVM program is to complement the larval management program by reducing the abundance of adult mosquitoes in an area, thereby reducing the number of eggs laid in breeding sites. Adult mosquito control is also intended to reduce the abundance of biting, infected adult mosquitoes in order to prevent them from transmitting WNV to humans and to break the mosquito - bird transmission cycle. In situations where vector abundance is increasing above acceptable levels, targeted adulticide applications using pesticides registered by EPA for this purpose can assist in maintaining vector abundance below threshold levels.”

226 It is evident the CDC emphasized the importance of mosquito density reduction by a combination of larval and adult control to maintain vector abundance below threshold levels to break the transmission cycle as suggested by in Bowman et al. (2005) and Wonham and Lewis (2008). The CDCs 2013 guidance did not discuss the relative importance of tracking mosquito abundance or mosquito IRs to provide the warning of increased human risk levels. There was no quantitative threshold of mosquito abundance or IR given that would trigger control measures.

To address this issue the Franklin County, 2002, model parameters

(parameters in Table 4.5 except the gamma parameter was changed to 4.75 per capita/week and the mosquito mortality rate was adjusted to were applied to the following equations:

where and are the mosquito and bird densities at the disease free equilibrium, which resulted in the estimated mosquito densities in Figure 4.34 and attained a R0 < 1. 227 A control strategy of a 12% weekly reduction in larval density and a 5% weekly reduction in adult mosquito densities, using parameters in Table 4.5

(except the gamma parameter was changed to 4.75 per capita/week), was then modeled and plotted and produced similar estimated densities as in Figure 4.34 and an estimated R0 < 1, as seen in Figure 4.35.

10000 100 Larva Exposed Susceptible Infected

5000 50

Exposed/Infected Larva/Susceptible

0 0 0 5 10 15 20 0 5 10 15 20 Time Time

1000 30

900 20 800 10

700 Infected birds Susceptible birds

600 0 0 5 10 15 20 0 5 10 15 20 Time Time

Figure 4.34. Estimated mosquito densities modeled with parameters in Table 4.5, except the gamma parameter was changed to 4.75 per capita/week and

per capita per week (10 days instead of 12 days). Time (x-axis) is in weeks, week 0 corresponds to epidemiological week 20, and week 20 represents epidemiological week 40 for Franklin County, Ohio, 2002.

228

10000 100 Larva Exposed Susceptible Infected

5000 50

Exposed/Infected Larva/Susceptible

0 0 0 5 10 15 20 0 5 10 15 20 Time Time

1000 30

900 20 800 10

700 Infected birds Susceptible birds

600 0 0 5 10 15 20 0 5 10 15 20 Time Time

Prior to the application of control methods, the estimated mosquito densities at week 31 were approximately 2500 Sm, 450 Em, and 400 Im, totaling 3,350 mosquitoes.

After the application of the model-induced control methods, the estimated mosquito densities during week 31 were approximately 2000 Sm, 60 Em, and 40 Im, totaling 2,100 adult mosquitoes. These densities represent an estimated 37.3% reduction of total adult

229 mosquitoes and a 10-fold reduction in Im. The actual density reduction of adult mosquitoes from week 31 to week 33 trap data was 36%, possibly caused by actual adult and larval control, meteorological influences or sampling errors as discussed in Chapter

1. Even though actual to predicted total density decreased between weeks 31 to 33 at similar levels (36% and 37.3%), week 33 actual IR failed to decrease. If these estimated control strategies were applied during week 31, and resulted in an actual 10-fold reduction in Im density, this action may have reduced the mosquito IR at week 33 to below 48/1000 and prevented the outbreak-level of human case onsets at week 35. It may be speculated that even with a 36% decrease in actual density, there was not a 10-fold reduction in actual Im by adult control methods.

As discussed, the limitations and strengths of this study were centered on the use of MM to predict WNV hazards and transmission risks. MM provided insights that would not have been gained from statistical analyses of drivers of WNV infection. Quantitative model fitting would have resulted in more accurate parameters. Parameter estimation presented challenges to the modeling efforts, but the most biologically relevant and reasonable parameters to reflect local conditions were chosen. Using the most real-to-life estimates of initial densities, parameters and constants is critical to the improvement of model predictions.

Applying real-life control strategies to the MM revealed the difficulty in connecting modeling theory to the use of models in practical control situations. For example, actual Franklin County, 2002, week 31 to 33 mosquito IR outcomes did not meet model expectations even though total mosquito density was reduced. Theoretically, attaining R0 <1 by calculating simulations of assumed adult or larval reduction does not

230 represent real-life adult or larval control when there is little assurance that the mosquitoes, whose lifespan is being reduced by adulticiding are infectious, that 100% of adult and larval control is efficacious, or that sampling errors caused a misrepresentation of the estimate of IRs.

4.6 Conclusion

The results of this study successfully answered the research questions: 1) A model was developed which had temporally similar mosquito infection density peaks for Ohio.

2) The model in this study appeared generalizable to multiple counties and different mosquito control seasons. 3) Density was shown to be an important factor in the transmission of WNV hazards, because models that were manipulated to reduce mosquito densities also provided reduction of Im and reduced R0 to <1. From the results of this preliminary research, focusing on density peak and sub-peak reductions may be as important as making control decision solely based upon increasing mosquito IRs. 4) It appeared that a combination of larval and adult control was the most cost-effective method to reduce hazards from WNV, and that applying combined control methods could produce similar reductions in R0 as adjusting the mosquito death rate parameter in the R0 formula to attain a R0 <1.

The introduction of a temperature and time modulated bite rate provided insights on the effect of weather on the biological functions of the mosquito. MM provided the opportunity to advance an understanding of the dynamics of WNV transmission between model components and ecological influences, and to experiment with control strategies that theoretically could prevent outbreaks. The relevance of the research in this Chapter is that it provided methods to the bridge between vector control theory and its practical

231 applications to assist vector control managers to make informed and cost-effective adult and larval control decisions to reduce the hazards to their communities.

Commitments have been received to collaborate with the Columbus, Ohio, Public

Health Department to test the application of the model during the 2012 mosquito control season. Future directions for this research include: 1) calibrating the model using the density of actual mosquitoes instead of IRs; 2) refining the model and its application protocol and methods after its initial use during the summer of 2012; 3) to quantitatively calibrate the model to fit actual mosquito infection rate data; 4) validating the model on a dataset different from the data used for calibration; 5) introducing additional meteorological functions into its components such as precipitation, and the PDI, and most importantly; and,6) continuing research with the same dataset using mosquito density as the outcome variable in statistical models analyzing meteorological variables as predictors, and experimenting with using mosquito density as the predictor variable to analyze its effect on mosquito IRs and human WNV case onsets on the 2012 dataset. This last research objective is important due to the multitude of errors that are introduced when calculating mosquito IRs using the MIR or MLE methods as described in Chapter 1 of this dissertation.

232

Chapter 5: Synthesis, public health implications, and future research

The overall research objective of this study was to understand the biological and meteorological drivers associated with WNV infection in mosquitoes and humans, and to develop a predictive model to inform public health surveillance and intervention practices. To meet this objective, novel hypothesized meteorologically-based indices, mosquito IRs, mosquito density, WNV positive birds, and human case onset dates were analyzed by descriptive statistics; human case onset dates, mosquito infection rates (IRs), and the indices were analyzed with regression modeling; and the results of these methods were used to inform the development and calibration of a predictive mathematical model.

This research was unique because the indices that were constructed and informed with temperature, precipitation, and the Palmer Drought Index represented temporal phases of the mosquitoes lifecycle (eggs, larvae, pupae, adult); key biological periods of the mosquito such as the gonotrophic cycle and extrinsic incubation period, which determines the biting rate and the rate at which the mosquito becomes infectious after being infected, respectively; and ecological periods that were theorized as important for the preparation of the oviposition (egg laying) site.

Chapter 2 presented the results of a descriptive analysis designed to identify observable patterns, trends, or associations among weekly mosquito density, mosquito

IRs, WNV positive birds, and human case onsets, and between mosquito IRs and the

233 constructed meteorologically-based indices representing phases of the mosquito life cycle and ecology, and informed with T, CP, and PDI. The trends identified were: 1) weekly mean temperature (T) increases from 21°C-26°C during the gonotrophic (GON2) time- delayed index, from 24°C-25°C during the eggs-to-hatch (E_H3) time-delayed index, and an average of 2°C overwinter (December, January, February); 2) Palmer Drought Index

(PDI) decreases from -1.25 to -2.75 during oviposition site development (OVPSD and

POVPSD and POVPSD1 compared to the OVPSD and OVPSD1 time-delayed indices.

Chapter 3 fine-tuned the results from the analysis described in Chapter 2, to identify the statitiscally significant indices associated with increases in mosquito IRs, and to test the hypothesis that there was a statistically significant association between mosquito IRs and human cases onsets two weeks after the mosquitoes were trapped. The following statistically significant indices were associated with increases in mosquito IRs:

1) an increase in T during the oviposition (OVP) index, the gonotrophic (GON2), egg-to- hatch (E_H3), oviposition site development (OVPSD1), preoviposition site development

(POVPSD), and overwintering time-delayed indices; 2) a decrease in CP during the

E_H3 and OVPSD1 time-delayed indices; 3) an increase in CP during the POVPSD time- delayed index; and, 4) a decrease in the PDI during the OVPSD1 and POVPSD time- delayed indices. Also, an increase in mosquito IR was found to be associated with a

234 significant increase in WNV infection in humans within two weeks after the week of trapping.

Chapter 4 used dynamic mathematical modeling methods to gain insights into the behavior of the complex relationship between mosquito and bird biology and how this relationship effected WNV transmission. The model was fit to Ohio 2002 mosquito IRs and calibrated to fit Franklin County and Cuyahoga County 2002 and 2003 mosquito IRs by manipulating the bite rate parameter and adding a bite rate function that was time and temperature dependent. The introduction of temperature into the model as a time variable bite rate regression function (of the effects of temperature on the length of the gonotrophic cycle) using a smoothed polynomial curve fit of temperature during the 2002

Franklin County mosquito control season, resulted in the reduction the peak Im density by 30%, increased Em and Im densities between weeks 28 and 32 by 25%, but did not shift density peaks. Larvae and adult mosquito control interventions were tested using the model to gain insight on the effect of control methods on infectious mosquito density. To connect the model with real-life mosquito control, combinations of weekly reductions of adult and larvae were added to the model. The most cost-efficient and effective control strategy appeared to be a 10% larvae and 30% adult control. The weekly bite rate was increased from 4.5 per mosquito per week to 5.5 and the mosquito natural mortality rate was decreased from 0.59 per mosquito per week to 0.33, and the results were that peak

Em and Im density occurred 5 weeks earlier and increased by a factor of 10, respectively.

235 adjusted to a 12% weekly larval reduction combined with a 5% weekly adult reduction to produce a similar infected mosquito density and an estimated R0 < 1

The public health implications of this study should be realized with continued research on connecting the descriptive, statistical and mathematical model outcomes to real-life applications of mosquito vector control. The knowledge gained from continuing this translational research at the county level should improve the predictive capacity of the modeling in order to prevent and control WNV in humans. To strengthen the translational nature of this study, a predictive tool needs to be developed which combines the lessons learned from all of the analyses into one easy-to-use package, complete with instructions, methods, and processes. These methods should include tracking of meteorological data, applying the knowledge learned in real-time from the results of the descriptive and statistical analyses of the indices that were significantly associated with increase in mosquito IRs, and using mathematical modeling and the evidence associating mosquito density increases with IRs to assist in the insights and the science needed to make decisions on the methods and timing of control strategies. Meteorological data could be tracked in real-time, converted up to each trap week to the indices developed in

Chapter 2, plotted by week up to the current trap week to record trends and patterns; predictive mathematical modeling could be performed during each trap week and informed with functions of real-time meteorologically-based statistically significant indices up to the trapping week and including one to two weeks of predicted weather after the trap week. With continued application and refinement of the methods, improvement should be seen in the capacity to predict WNV transmission hazards and public health risks.

236 Future descriptive, statistical and mathematical modeling should be interpreted with some degree of caution because of the unavoidable systematic errors that were discovered during this research. These errors can be described as follows: 1) County- level data aggregation did not have the resolution to analyze local or street-level WNV transmission foci parameters; 2) County-level errors were present due to the lack of consistency of mosquito management programs between and within counties, such as brew mixtures, trapping time frames, the use of larvicides and/or adulticides, public education and other prevention strategies, as summarized with the mosquito control survey data; 3) The data used for this study only included Culex spp, which could bias the interpretation of any future studies of other mosquito species that would attempt to descriptively or statistically associate density, IR and human WNV case onsets, and future mathematical modeling; 4) Surveillance methods typically did not capture a randomly selected representative sample of the mosquito population; 5) The myriad of errors that were introduced by the use of the mosquito IR estimation methods, MIR and

MLE, including sampling errors; 6) The mathematical modeling did not include any migration of birds into the transmission system; 7) The time frames phases of the mosquito life cycle and mosquito ecology represented by the indices created in Chapter 2 were estimated from the best known literature values; 8) The parameters and functions that were integrated into the mathematical model and the results of the model were estimated; 8a) The mathematical model was based upon a constant mosquito bite-rate regardless of bird densities; 9) The Ohio Department of Health mosquito IR data during

2002 compared to 2003 to 2006 did not receive the same level of quality assurance/quality control; 10) The captured datasets of human cases, and WNV positive

237 mosquitoes and birds were limited due to the human cases, mosquitoes and birds that were not reported or underreported; 11) The results of descriptive and analytical statistics were estimates of actual associations that may have existed between predictors and outcomes; and, 12) The results of mathematical modeling were “simplifications of complex real systems”(Hethcote, (n.d.)).

Even with these systematic errors the descriptive and analytical analyses and the mathematical model were all highly effective in providing preliminary insights into the drivers of WNV transmission dynamics and human health risks. The results of the descriptive analyses were closely related to the results of the statistical analyses, with deference given to an experiential bias from this writer. The mathematical modeling resulted in major and minor peaks of infectious mosquito density which were temporally similar to mosquito IR peaks and sub-peaks.

The challenges that this writer had to overcome to move WNV modeling theory to its practical application were significant. As discussed in Chapter 4, the nexus between theory and practice was found by the parameterization of the reproductive number, R0, formula to result in a R0 <1. Once the mosquito death rate parameter was adjusted, the model was plotted which resulted in graphical representations of exposed and infected mosquito densities. Next, adjustments were made to the weekly control parameters used in the mosquito control model to produce a plot with similar densities. From a qualitatively perspective, the control strategy used to reduce R0 <1, should, theoretically, prevent human disease outbreaks, although the resulting model remained an estimate of the densities needed to prevent human disease. More research will be needed to fine-tune

238 this method by comparing its model-induced density reductions with actual mosquito density reductions to determine if actual density is comparable to predicted density.

An overall concern of this study was the use of estimates of mosquito IRs to drive control decisions, such as mosquito abundance, number of positive pools, percent positive pools, IR, MLE, density of infected mosquitos (DIM) and the vector index (VI) as described in CDC (2013c). An additional concern was the consideration given by this writer to the use of density metrics, including density reduction-based modeling, to assist mosquito control managers in these control decisions, to supplant or as a substitute for

IR, MLE, DIM and VI.

Whatever metric is used to decide on the timing and type of control strategies does not alleviate the apprehension that the decision may be made in error, or that the decision could result in false negative or false positive results. For the purposes of this discussion, a false positive decision would occur when surveillance sampling resulted in an increase in density and indicated a predicted future increase in IRs, or sampling resulted in an increase in IRs and indicated the potential for a future outbreak, a decision was made to apply control methods during the week of sampling, and IRs do not increase or an outbreak does not occur; and a false negative decision would occur when surveillance sampling resulted in no increase (or a decrease) in density and indicated no predicted future increase in IRs, or sampling resulted in no increase (or a decrease) in IRs and did not indicate the potential for a future outbreak, a decision was made not to apply control methods, and IRs increased or an outbreak occurred. If the assumption was made that factors other than the control methods were responsible for IRs not increasing or outbreaks not occurring, the false positive decision would place the public at risk from

239 the application of insecticides without a comparable risk of WNV transmission and the control agency would incur unjustified costs with no public health returns. If the assumption was made that the control methods were responsible for IRs not increasing or outbreaks not occurring, the false positive decision could be lauded as successful disease prevention. A false negative decision would place the public at risk from a potential

WNV outbreak. A true positive decision would be a scenario when surveillance sampling resulted in increases in density and IRs and indicated a predicted future increase in IRs or an outbreak, respectively, a decision was made to apply control methods the same week as sampling, and IRs increased or an outbreak occurred even with the application of controls. A true negative decision would be a scenario when surveillance sampling resulted in no increases in density and IRs and indicated no predicted future increase in

IRs or an outbreak, respectively, a decision was made not to apply control methods, and

IRs do not increase and an outbreak does not occur. It is evident that the true negative and the false positive (under the assumption that the control methods were efficacious) decisions would be the scenarios acceptable to public health protection. The false negative and true positive scenarios would be the most dangerous to public health protection.

A review of the data presented in Figures 4.33 and 5.1 will assist in this discussion and is based upon these assumptions: 1) that the adulticiding programs in 2002 at the Franklin County and Columbus City health departments were in their first year of experience in WNV control, and their control programs may have been further developed and efficacious by 2003; 2) that increases in mosquito abundance may drive increases in mosquito IR with a one, two, or three-week lag; 3) that there is less time between the

240 temporal lags of density, IRs, and human case onsets during week 30 to week 38, and a longer time frame between weeks 23 to 30 and 38 to the end of the season, i.e., these trends vary with time and temperature; 4) that the application of control methods was made during the same week the data on density and IRs were collected; 5) that control methods were localized at a minimum of a 0.8 km radius from the trap location that produced increases in density or IRs, and the weekly data presented was countywide; 6) that human case onset dates are not known in real time; 7) that human case onsets were significantly associated with increases in IRs within two weeks but also subject to assumption number 3; and, 8) as discussed in Chapter 1, the estimation of IRs are subject to errors.

Density, Infection Rate, Human Case Onset Week, and Adulticiding, Franklin County, 2003 35

25 Density(Density/100) IRMLE 20 Human Cases Adulticide(1=yes, 0=no) 15

5 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 1 0 0 0 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 Week Number

Figure 5.1 Mosquito density (actual density divided by 100) and average infection rate by trap week, adulticiding by control week, and human cases by onset week for Franklin

County, 2003 241

For the purposes of this discussion, an example of a false negative decision would be one that was made at week 30 in Figure 4.33, when density decreased from previous weeks, and even though IR increased from week 29, a decision was made not to apply control methods, but IRs exponentially increased by week 30. A different decision could have been made with the application of the previous assumptions: If it were assumed that the density peak at week 27 could have driven the IR peak at week 30, that density levels at weeks 28 and 29 could have driven IRs at weeks 31 and 32, that week 30 was a transition week, and that the density peak at week 31 could have driven the IR peak at week 33, which would mean that the control decision would have been made based upon a three-week history of density and IRs and not on the surveillance data gathered during week 30. Consequently, the prudent decision could have been to apply control methods during week 30, which may have reduced IRs at week 33 to prevent the human case outbreak during week 35.

A true positive scenario could be illustrated by the decision to apply control methods during week 33, Figure 4.33, but IRs increased at week 35 and a human case onset occurred at week 37. A true negative decision not to apply control methods was made during week 26, Figure 4.33, because no increases in density or IRs were seen during weeks 28 and 29. In Figure 5.1, a false negative decision was made to not apply control measures during week 31 possibly because of the decreasing IRs from week 29. If it was assumed that the density increases between weeks 28 and the peak at week 31 could have driven the peak in IRs at week 33 and this IR peak could have been

242 responsible for the human cases at weeks 34 and 35, then the prudent decision would have been to apply control measures during week 31.

This writer questioned the relative weight given to mosquito density and mosquito

IRs when making control decisions at the practice-level, given the inherent errors in the estimation methods of mosquito IRs as described in Chaper 1. Continued research is critical to increase the understanding the importance of density reduction to the prevention or reduction of human WNV transmission risks. Mosquito control program managers need to continue to make the connections, as advised by CDC (2013c), among density thresholds, mosquito IRs, human case onsets and the cost effectiveness of their control strategies. Evaluating the success of control strategies by post application metrics and remaining diligent to understanding the reasons for false negative and true positive scenarios as described in this section may increase the success of preventing WNV human disease.

With decreasing funds to support mosquito control programs across the U.S., as suggested by Del Rosario, Richards, Anderson, and Balanay (2014), a paradigm shift may be necessary in the practical application of mosquito control strategies to analyze mosquito control programs on their cost-effectiveness. Metrics should be developed to measure program cost against outcomes that program managers can control, and that is most likely the costs associated with surveillance, testing, and the reduction of mosquito density (given the inherent errors in sampling). It is this writer’s opinion from personal experiences in mosquito control management that the costs that reduce the effectiveness of surveillance are those involved with maintaining sampling locations and frequencies that are not historically and scientifically predictive of WNV transmission hazards or

243 public health risks; the costs that reduce the effectiveness of testing are those involved with testing for positive mosquitoes or birds during known endemic or enzootic periods, when the hazards of WNV transmission and risks to public health are known to be low, and the cost versus benefits of public education and source reduction may outway the cost vs benefits of continued testing, i.e, oversampling; the costs that reduce the effectiveness of applying control strategies are when predictable and scientifically sound hazards of WNV transmission and public health risks exist and strategies are deployed that do not quantifiably reduce mosquito density, or result in false negative or true positive decisions as described in this section.

While the local and state political and economic realities have an overwhelming influence on the programmatic elements and funding of local mosquito control programs, the results of this research should be refined through further study and be applied to cost- effectively reduce WNV transmission hazards and public health risks.

244

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258

Appendix A: Demographic characteristics of persons with WNV disease

Nonneuroinvasive (N = 17,139) Neuroinvasive (N = 11,822) Characteristic No. (%) No. (%)

Sex 8,972 (52) 6,887 (58) Male 8,124 (47) 4,905 (41) Female 43 (<1) 30 (<1) Unknown Age Median age (range) 47 yrs (1 mo--97 yrs) 57 yrs (1 mo--99 yrs) Age group, yrs 237 (1) 179 (2) 0--9 1,130 (7) 410 (3) 10--19 1,423 (8) 707 (6) 20--29 2,627 (15) 1,120 (9) 30--39 4,150 (24) 1,884 (16) 40--49 3,608 (21) 1,995 (17) 50--59 1,969 (12) 1,925 (16) 60--69 1,812 (11) 3,549 (30) ≥70 Unknown 183 (1) 53 (<1)

Race 10,743 (63) 5,875 (50) White 194 (1) 611 (5) Black Native American 287 (2) 163 (1) or Alaskan Native Asian or Pacific 64 (<1) 41 (<1) Islander Unknown 5,851 (34) 5,132 (43)

Ethnicity 927 (5) 986 (8) Hispanic 8,574 (50) 4,591 (39) Non-Hispanic 7,638 (45) 6,245 (53) Unknown

259

Appendix B: Demographic characteristics by clinical syndrome

260 Encephalitis (N = Acute flaccid paralysis Meningitis (N = 3,930) Unspecified (N = 79) Characteristic 7,502) (N = 311) No. (%) No. (%) No. (%) No. (%)

Age 60 (1 mo--99 46 (1 mo--95 55 (1--94 yrs) 63 (5--90 yrs) Median age yrs) yrs) (range) Age group, yrs 98 (1) 73 (2) 7 (2) 1 (1) 0--9 188 (3) 214 (6) 5 (2) 3 (4) 10--19 307 (4) 386 (10) 13 (4) 1 (1) 20--29 479 (6) 609 (15) 27 (9) 5 (6) 30--39 952 (13) 873 (22) 54 (17) 5 (6) 40--49 1,188 (16) 718 (18) 77 (25) 12 (15) 50--59 1,378 (18) 479 (12) 55 (18) 13 (17) 60--69 2,890 (38) 554 (14) 67 (22) 38 (48) ≥70 22 (<1) 24 (1) 6 (2) 1 (1) Unknown

Sex 4,475 (60) 2,175 (55) 192 (62) 45 (57) Male 3,007 (40) 1,746 (44) 119 (38) 33 (42) Female 20 (<1) 9 (<1) 0 0 1 (1) Unknown

Race 3,425 (46) 2,225 (57) 225 (72) 0 0 White 439 (6) 149 (4) 23 (7) 0 0 Black Native 88 (1) 70 (2) 5 (2) 0 0 American or Alaskan Native Asian or 12 (<1) 28 (1) 1 (<1) 0 0 Pacific Islander 3,538 (47) 1,458 (37) 57 (18) 79 (100) Unknown

Ethnicity 425 (6) 523 (13) 38 (12) 0 0 Hispanic 2,666 (35) 1,730 (44) 195 (63) 0 0 Non-Hispanic 4,411 (59) 1,677 (43) 78 (25) 79 (100) Unknown

261

Appendix C: WNV case definition and clinical presentations

262 Arboviral diseases, neuroinvasive and non-neuroinvasive

Subtype(s)

 California Serogroup Virus Diseases  Eastern Equine Encephalitis Virus Disease  Powassan Virus Disease  St. Louis Encephalitis Virus Disease  West Nile Virus Disease  Western Equine Encephalitis Virus Disease

Background

Arthropod-borne viruses (arboviruses) are transmitted to humans primarily through the bites of infected mosquitoes, ticks, sand flies, or midges. Other modes of transmission for some arboviruses include blood transfusion, organ transplantation, perinatal transmission, consumption of unpasteurized dairy products, breast feeding, and laboratory exposures.

More than 130 arboviruses are known to cause human disease. Most arboviruses of public health importance belong to one of three virus genera: Flavivirus, Alphavirus, and Bunyavirus.

California serogroup viruses include: California encephalitis, Jamestown Canyon, Keystone, La Crosse, Snowshoe hare, and Trivittatus viruses)

Clinical Description

Most arboviral infections are asymptomatic. Clinical disease ranges from mild febrile illness to severe encephalitis. For the purposes of surveillance and reporting, based on their clinical presentation, arboviral disease cases are often categorized into two primary groups: neuroinvasive disease and non-neuroinvasive disease.

Neuroinvasive disease Many arboviruses cause neuroinvasive disease such as aseptic meningitis, encephalitis, or acute flaccid paralysis (AFP). These illnesses are usually characterized by the acute onset of fever with stiff neck, altered mental status, seizures, limb weakness, cerebrospinal fluid (CSF) pleocytosis, or abnormal neuroimaging. AFP may result from anterior ("polio") myelitis, peripheral neuritis, or post-infectious peripheral demyelinating neuropathy (i.e., Guillain-Barré syndrome). Less common neurological manifestations, such as cranial nerve palsies, also occur.

Non-neuroinvasive disease Most arboviruses are capable of causing an acute systemic febrile illness (e.g., West Nile fever) that may include headache, myalgias, arthralgias, rash, or gastrointestinal

263 symptoms. Rarely, myocarditis, pancreatitis, hepatitis, or ocular manifestations such as chorioretinitis and iridocyclitis can occur.

Clinical Criteria

A clinically compatible case of arboviral disease is defined as follows:

 Neuroinvasive disease o Fever (≥100.4°F or 38°C) as reported by the patient or a health-care provider, AND o Meningitis, encephalitis, acute flaccid paralysis, or other acute signs of central or peripheral neurologic dysfunction, as documented by a physician, AND o Absence of a more likely clinical explanation.  Non-neuroinvasive disease o Fever (≥100.4°F or 38°C) as reported by the patient or a health-care provider, AND o Absence of neuroinvasive disease, AND o Absence of a more likely clinical explanation.

Laboratory Criteria for Diagnosis

 Isolation of virus from, or demonstration of specific viral antigen or nucleic acid in, tissue, blood, CSF, or other body fluid, OR  Four-fold or greater change in virus-specific quantitative antibody titers in paired sera, OR  Virus-specific IgM antibodies in serum with confirmatory virus-specific neutralizing antibodies in the same or a later specimen, OR  Virus-specific IgM antibodies in CSF and a negative result for other IgM antibodies in CSF for arboviruses endemic to the region where exposure occurred, OR  Virus-specific IgM antibodies in CSF or serum.

Case Classification

Probable

Neuroinvasive disease A case that meets the above clinical criteria for neuroinvasive disease and the following laboratory criteria:

 Virus-specific IgM antibodies in CSF or serum but with no other testing.

264 Non-neuroinvasive disease A case that meets the above clinical criteria for non-neuroinvasive disease and the laboratory criteria for a probable case:

 Virus-specific IgM antibodies in CSF or serum but with no other testing.

Confirmed

Neuroinvasive disease A case that meets the above clinical criteria for neuroinvasive disease and one or more the following laboratory criteria for a confirmed case:

Non-neuroinvasive disease A case that meets the above clinical criteria for non-neuroinvasive disease and one or more of the following laboratory criteria for a confirmed case:

Epidemiologic Classification

Imported arboviral diseases Human disease cases due to Dengue or Yellow fever viruses are nationally notifiable to CDC using specific case definitions. However, many other exotic arboviruses (e.g., Chikungunya, Japanese encephalitis, Tick-borne encephalitis, Venezuelan equine encephalitis, and Rift Valley fever viruses) are important public health risks for the United States as competent vectors exist that could allow for sustained transmission upon establishment of imported arboviral pathogens. Health-care providers and public health officials should maintain a high index of clinical suspicion for cases of potentially exotic

265 or unusual arboviral etiology, particularly in international travelers. If a suspected case occurs, it should be reported to the appropriate local/state health agencies and CDC.

Comment(s)

Interpreting arboviral laboratory results:

 Serologic cross-reactivity: In some instances, arboviruses from the same genus produce cross-reactive antibodies. In geographic areas where two or more closely- related arboviruses occur, serologic testing for more than one virus may be needed and results compared to determine the specific causative virus. For example, such testing might be needed to distinguish antibodies resulting from infections within genera, e.g., flaviviruses such as West Nile, St. Louis encephalitis, Powassan, Dengue, or Japanese encephalitis viruses.  Rise and fall of IgM antibodies: For most arboviral infections, IgM antibodies are generally first detectable at 3 to 8 days after onset of illness and persist for 30 to 90 days, but longer persistence has been documented (e.g, up to 500 days for West Nile virus). Serum collected within 8 days of illness onset may not have detectable IgM and testing should be repeated on a convalescent-phase sample to rule out arboviral infection in those with a compatible clinical syndrome.  Persistence of IgM antibodies: Arboviral IgM antibodies may be detected in some patients months or years after their acute infection. Therefore, the presence of these virus-specific IgM antibodies may signify a past infection and be unrelated to the current acute illness. Finding virus-specific IgM antibodies in CSF or a fourfold or greater change in virus-specific antibody titers between acute- and convalescent-phase serum specimens provides additional laboratory evidence that the arbovirus was the likely cause of the patient’s recent illness. Clinical and epidemiologic history also should be carefully considered.  Persistence of IgG and neutralizing antibodies: Arboviral IgG and neutralizing antibodies can persist for many years following a symptomatic or asymptomatic infection. Therefore, the presence of these antibodies alone is only evidence of previous infection and clinically compatible cases with the presence of IgG, but not IgM, should be evaluated for other etiologic agents.  Arboviral serologic assays: Assays for the detection of IgM and IgG antibodies commonly include enzyme-linked immunosorbent assay (ELISA), microsphere immunoassay (MIA), or immunofluorescence assay (IFA). These assays provide a presumptive diagnosis and should have confirmatory testing performed. Confirmatory testing involves the detection of arboviral-specific neutralizing antibodies utilizing assays such as plaque reduction neutralization test (PRNT).  Other information to consider. Vaccination history, detailed travel history, date of onset of symptoms, and knowledge of potentially cross-reactive arboviruses known to circulate in the geographic area should be considered when interpreting results.

266

Appendix D: Ohio Christmas Bird Count locations

267

268

Appendix E: Ohio first order weather stations

269

270

Appendix F: Ohio day length regions

271

272

Appendix G: Ohio weather districts

273

274

Appendix H: Mosquito control program index survey

275 Dear Colleague, My name is Paul Rosile and I retired as the Environmental Health Director at Franklin County Public Health in July of 2012 . I am writing you to ask for a few minutes of your time to complete the attached survey about your mosquito control program from 2002 to 2006. These data will be used as a variable as part of my doctoral dissertation at the Ohio State University College of Public Health on environmental and ecological factors affecting the incidence of West Nile Virus (WNV). Your jurisdiction is important to my analysis because it had either probable or confirmed human WNV disease case(s) reported to the Ohio Department of Health (ODH) or mosquitoes submitted to the ODH lab for testing. The human case data was obtained with permission from the Ohio Department of Health, with only “county” and “jurisdiction” of case location as identifiers. The overarching objective of this research is to determine the environmental and ecological factors associated with WNV disease and to develop a predictive model for use by local health departments as part of their control progam. Directions: Please indicate your jurisdiction in the first row. For each year, please mark the components which applied to your mosquito control program. The extent, scope, or quality of your individual components is not an objective of this survey. If you integrated any degree of the component into your overall program, please mark the component. The components represent parts of an integrated mosquito control program. Please return the survey even if you had no components and mark “n/a” in the “Total”. Your answers will be analyzed in aggregate at a state level of analysis. Please return the completed survey in the self-addressed envelope provided by July 31, 2013, or e- mail it back to me as a PDF. If you have any questions, you can call me at 614-419-5900, e-mail at [email protected]. If you want an electronic copy of my research prospectus (similar to a grant proposal), please mark the circle following your jurisdiction, followed by your e-mail address. I thank you in advance for your participation.

*May include consultation on community, neighborhood, or individual level on breeding site identification and elimination **May include larvae surveillance ***Must include adult mosquito trapping with light or gravid traps ****May include both density (number) and WNV positive mosquitoes

276

277

Appendix I: Adult and larvae control survey

278 Dear Colleague,

I recently received from you a completed survey describing your WNV mosquito control program during the years 2002-2006. Thank you again for participating!!! I am submitting this request for additional information on your truck based spraying and larviciding/pupaciding mosquito control activities. What I need are your records of when you sprayed or larvicided/pupacided during the years of 2002-2006. This information is important for my research because it may help explain the weekly variation in mosquito density, Minimum Infection Rates (MIR), and the incidence of human cases, while also taking into account weather, ecological, and biological variables. The final model that will be developed with these data will hopefully help us predict WNV disease in humans and mosquitoes, which may become an important addition to our predictive tools due to the lack of WNV testing from ODH. I understand that your records are subject to a retention policy unique to your department, and that these requests are time consuming. If I can be of any personal assistance in reviewing your records at your department to alleviate your time, please call me at 614-419-5900 or e-mail at [email protected] to arrange a mutually acceptable time. I have attached a spreadsheet that may be useful in reporting this information. For each week and year, please mark “Yes-SPRAY”, “No-SPRAY”, “Yes-LAR”, or “No-LAR” in the appropriate column. The extent, scope, or quality of your spraying or larvidicing/pupaciding is not an objective of this records request. If you sprayed or larvicided/pupacided at any level during the week in question, please mark the week “Yes”. Your answers will be analyzed in aggregate at a state level of analysis. If you complete the attached form, please return it in the self-addressed envelope provided, or return it as a PDF by e-mail; or return a copy of your own records as a PDF or by regular mail to the address below by June 7, 2013. If you have any questions, you can call me at 614-419-5900, or e-mail at [email protected]. I thank you in advance for your participation in this research. P.S. I am going to need to validate the model using real time data in one or two of the following jurisdictions, if you have an active surveillance and testing (RAMP, VEC-TEST, RT-PCR) program this year: Columbus City, Franklin County, Cuyahoga County, Hamilton County, Cincinnati City, Summit County, Butler County, and Lake County. Please let me know by e-mail if you are interested in working with me on the final development of the predictive model.

Sincerely,

Paul Rosile, MPH, RS Mailing address: PhD Candidate Mr. Paul Rosile The Ohio State University College of Public Health 334 Catawba Ave. Retired, EH Director, Franklin County Public Health Westerville, Ohio 43081

279 Week begin date Week end date Week # Yes-SPRAY No-SPRAY Yes-LAR No-LAR

June 2, 2002 June 8, 2002 2.23

June 9, 2002 June 15, 2002 2.24 June 16, 2002 June 22, 2002 2.25 June 23, 2002 June 29, 2002 2.26 June 30, 2002 July 6, 2002 2.27 July 7, 2002 July 13, 2002 2.28 July 14, 2002 July 20, 2002 2.29 July 21, 2002 July 27, 2002 2.30 July 28, 2002 August 3, 2002 2.31 August 4, 2002 August 10, 2002 2.32 August 11, 2002 August 17, 2002 2.33 August 18, 2002 August 24, 2002 2.34 August 25, 2002 August 31, 2002 2.35 September 1, 2002 September 7, 2002 2.36 September 8, 2002 September 14, 2002 2.37 September 15, 2002 September 21, 2002 2.38 September 22, 2002 September 28, 2002 2.39

September 29, 2002 October 5, 2002 2.40

June 1, 2003 June 7, 2003 3.23

June 8, 2003 June 14, 2003 3.24 June 15, 2003 June 21, 2003 3.25 June 22, 2003 June 28, 2003 3.26 June 29, 2003 July 5, 2003 3.27 July 6, 2003 July 12, 2003 3.28 July 13, 2003 July 19, 2003 3.29 July 20, 2003 July 26, 2003 3.30 July 27, 2003 August 2, 2003 3.31 August 3, 2003 August 9, 2003 3.32 August 10, 2003 August 16, 2003 3.33 August 17, 2003 August 23, 2003 3.34 August 24, 2003 August 30, 2003 3.35 August 31, 2003 September 6, 2003 3.36 September 7, 2003 September 13, 2003 3.37 September 14, 2003 September 20, 2003 3.38 September 21, 2003 September 27, 2003 3.39

September 28, 2003 October 4, 2003 3.40

May 30, 2004 June 5, 2004 4.23

June 6, 2004 June 12, 2004 4.24 280 June 13, 2004 June 19, 2004 4.25 June 20, 2004 June 26, 2004 4.26 June 27, 2004 July 3, 2004 4.27 July 4, 2004 July 10, 2004 4.28 July 11, 2004 July 17, 2004 4.29 July 18, 2004 July 24, 2004 4.30 July 25, 2004 July 31, 2004 4.31 August 1, 2004 August 7, 2004 4.32 August 8, 2004 August 14, 2004 4.33 August 15, 2004 August 21, 2004 4.34 August 22, 2004 August 28, 2004 4.35 August 29, 2004 September 4, 2004 4.36

September 5, 2004 September 11, 2004 4.37

September 12, 2004 September 18, 2004 4.38 September 19, 2004 September 25, 2004 4.39 September 26, 2004 October 2, 2004 4.40

May 29, 2005 June 4, 2005 5.22

June 5, 2005 June 11, 2005 5.23

June 12, 2005 June 18, 2005 5.24 June 19, 2005 June 25, 2005 5.25 June 26, 2005 July 2, 2005 5.26 July 3, 2005 July 9, 2005 5.27 July 10, 2005 July 16, 2005 5.28 July 17, 2005 July 23, 2005 5.29 July 24, 2005 July 30, 2005 5.30 July 31, 2005 August 6, 2005 5.31 August 7, 2005 August 13, 2005 5.32 August 14, 2005 August 20, 2005 5.33 August 21, 2005 August 27, 2005 5.34 August 28, 2005 September 3, 2005 5.35 September 4, 2005 September 10, 2005 5.36 September 11, 2005 September 17, 2005 5.37 September 18, 2005 September 24, 2005 5.38 September 25, 2005 October 1, 2005 5.39

May 28, 2006 June 3, 2006 6.22

June 4, 2006 June 10, 2006 6.23

June 11, 2006 June 17, 2006 6.24 June 18, 2006 June 24, 2006 6.25 June 25, 2006 July 1, 2006 6.26

281 July 2, 2006 July 8, 2006 6.27 July 9, 2006 July 15, 2006 6.28 July 16, 2006 July 22, 2006 6.29 July 23, 2006 July 29, 2006 6.30 July 30, 2006 August 5, 2006 6.31 August 6, 2006 August 12, 2006 6.32 August 13, 2006 August 19, 2006 6.33 August 20, 2006 August 26, 2006 6.34 August 27, 2006 September 2, 2006 6.35 September 3, 2006 September 9, 2006 6.36 September 10, 2006 September 16, 2006 6.37 September 17, 2006 September 23, 2006 6.38 September 24, 2006 September 30, 2006 6.39

282

Appendix J: Ohio human WNV cases per county, 2002 to 2006

283 County Number of Cases County Number of Cases Adams 2 Logan 2 Allen 7 Lorain 23 Ashtabula 3 Lucas 25 Athens 1 Madison 3 Auglaize 7 Mahoning 6 Brown 1 Marion 4 Butler 11 Medina 3 Champaign 3 Mercer 5 Clark 24 Miami 3 Clermont 8 Montgomery 11 Clinton 4 Morgan 1 Coshocton 1 Ottawa 2 Crawford 1 Paulding 3 Cuyahoga 285 Pickaway 1 Darke 7 Portage 3 Defiance 8 Preble 3 Erie 2 Putnam 9 Fairfield 1 Richland 1 Franklin 17 Ross 1 Fulton 8 Sandusky 1 Geauga 4 Scioto 1 Greene 4 Seneca 1 Guernsey 1 Shelby 2 Hamilton 40 Stark 15 Hancock 7 Summit 5 Hardin 3 Trumbull 5 Henry 2 Union 3 Holmes 5 Van Wert 2 Huron 2 Warren 4 Jackson 2 Washington 1 Knox 2 Wayne 8 Lake 9 Williams 3 Lawrence 1 Wood 14 Licking 6 Wyandot 6

284

Appendix K: Mosquito species data, 2002 to 2006

285 2002 2003 Species sumspp %spp pools pos %pos sumspp %spp pools pos %pos Aedes albopictus 850 0.45 221 28 12.67 1269 0.26 275 2 0.73 Aedes atropalpus 290 0.16 54 4 7.41 0 0 0 0 0 Aedes canadensis 12 0.01 8 2 25.00 0 0 0 0 0 Aedes cinereus 0 0 0 0 0 86 0.02 32 0 0 Aedes intrudens 0 0 0 0 0 1 0.00 1 0 0 Aedes japonicus 420 0.22 86 14 16.28 0 0 0 0 0 Aedes sollicitans 25 0.01 1 0 0.00 0 0 0 0 0 Aedes sp. 0 0 0 0 0 8 0.00 1 0 0.00 Aedes stimulans 4 0.00 4 0 0.00 0 0 0 0 0 Aedes triseriatus 1573 0.84 458 45 9.83 0 0 0 0 0 Aedes trivittatus 597 0.32 81 8 9.88 0 0 0 0 0 Aedes vexans 1223 0.65 286 31 10.84 25799 6.14 1579 10 0.63 Anopheles barberi 23 0.01 21 3 14.29 54 0.01 37 0 0.00 Anopheles crucians 0 0 0 0 0 0 0 0 0 0 Anopheles perplexans 3 0.00 2 0 0.00 3 0.00 2 0 0.00 Anopheles punctipennis 700 0.37 258 26 10.08 3015 0.72 874 3 0.34 Anopheles quadrimaculatus 196 0.10 107 14 13.08 1931 0.46 386 5 1.30 Anopheles spp. 3 0.00 2 0 0.00 1 0.00 1 0 0.00 Anopheles walkeri 2 0.00 2 0 0.00 17147 4.08 444 1 0.23 Coquillettidia perturbans 182 0.10 48 7 14.58 13177 3.14 512 3 0.59 Culex erraticus 131 0.07 50 6 12.00 11 0.00 9 0 0.00 Culex pipiens 2155 1.15 278 62 22.30 3008 0.72 305 0 0.00 Culex restuans 384 0.21 96 6 6.25 659 0.16 20 0 0.00 Culex salinarius 6 0.00 4 1 25.00 0 0 0 0 0 Culex spp. 178071 95.20 6099 1816 29.78 380053 90.44 11910 755 6.34 Culex territans 2 0.00 2 0 0.00 4 0.00 2 0 0.00 Culiseta inornata 3 0.00 3 1 33.33 4 0.00 4 0 0.00 Culiseta melanura 1 0.00 1 0 0.00 7 0.00 3 0 0.00 Culiseta morsitans 0 0 0 0 0 1 0.00 1 0 0.00 Culiseta sp. 0 0 0 0 0 1 0.00 1 0 0.00 Hippoboscidae 26 0.01 14 0 0.00 0 0 0 0 0 Ochlerotatus atropalpus 0 0 0 0 0 27 0.01 4 0 0.00 Ochlerotatus canadensis 0 0 0 0 0 0 0 0 0 0 Ochlerotatus cantator 0 0 0 0 0 3242 0.77 183 0 0.00 Ochlerotatus excrucians gr. 0 0 0 0 0 15 0.00 4 0 0.00 Ochlerotatus grossbecki 0 0 0 0 0 1 0.00 1 0 0.00 Ochlerotatus japonicus 0 0 0 0 0 167 0.04 39 1 2.63 Ochlerotatus sollicitans 0 0 0 0 0 320 0.08 148 0 0.00 Ochlerotatus sp. 0 0 0 0 0 36 0.01 3 0 0.00 Ochlerotatus sticticus 0 0 0 0 0 194 0.05 22 0 0.00 Ochlerotatus stimulans 0 0 0 0 0 1126 0.27 127 1 0.79 Ochlerotatus triseriatus 0 0 0 0 0 3511 0.84 1182 7 0.59 Ochlerotatus trivittatus 0 0 0 0 0 29868 7.11 1306 4 0.31 Orthopodomyia 0 0 0 0 0 1 0.00 1 0 0.00 Orthopodomyia alba 3 0.00 3 0 0.00 1 0.00 1 0 0.00 Orthopodomyia signifera 17 0.01 16 4 25.00 26 0.01 22 0 0.00 Orthopodomyia spp. 1 0.00 1 1 100.0 0 0 0 0 0 Psorophora ciliata 1 0.00 1 1 100.0 6 0.00 6 0 0.00 Psorophora columbiae 19 0.01 3 1 33.33 128 0.03 18 0 0.00 Psorophora cyanescens 0 0 0 0 0 1 0.00 1 0 0.00 Psorophora ferox 24 0.01 7 0 0.00 158 0.04 58 0 0.00 Psorophora horrida 12 0.01 3 0 0.00 40 0.01 3 0 0.00 Simuliidae 0 0 0 0 0 979 0.23 24 0 0.00 Unknownspecies 1 0.00 1 1 100.00 0 0 0 0 0 Uranotaenia sapphirina 86 0.05 18 0 0 691 0.16 173 0 0.00

286 2004 2005 Species sumspp %spp pools pos %pos sumspp %spp pools pos %pos Aedes albopictus 702 0.18 177 5 2.82 1231 0.32 322 14 4.35 Aedes atropalpus 0 0 0 0 0 3 0.00 2 0 0.00 Aedes canadensis 0 0 0 0 0 1 0.00 1 0 0.00 Aedes cinereus 1 0.00 1 0 0.00 0 0 0 0 0 Aedes intrudens 0 0 0 0 0 0 0 0 0 0 Aedes japonicus 0 0 0 0 0 7058 1.81 2189 15 0.69 Aedes sollicitans 0 0 0 0 0 0 0 0 0 0 Aedes sp. 0 0 0 0 0 1 0.00 1 0 0.0 Aedes stimulans 0 0 0 0 0 1 0.00 1 0 0.0 Aedes triseriatus 0 0 0 0 0 1062 0.27 557 7 1.26 Aedes trivittatus 0 0 0 0 0 371 0.10 104 0 0.00 Aedes vexans 7125 1.79 409 4 0.98 885 0.23 178 1 0.56 Anopheles barberi 18 0.00 16 0 0.00 64 0.02 45 0 0.00 Anopheles crucians 2 0.00 2 0 0.00 0 0 0 0 0 Anopheles perplexans 1 0.00 1 0 0.00 0 0 0 0 0 Anopheles punctipennis 573 0.14 174 0 0.00 229 0.06 162 0 0.00 Anopheles quadrimaculatus 159 0.04 57 1 1.75 149 0.04 52 0 0.00 Anopheles spp. 0 0 0 0 0 0 0 0 0 0 Anopheles walkeri 812 0.20 27 0 0.00 0 0 0 0 0 Coquillettidia perturbans 902 0.23 52 0 0.00 1704 0.44 64 0 0.00 Culex erraticus 27 0.01 17 0 0.00 16 0.00 4 0 0.00 Culex pipiens 994 0.25 102 0 0.00 0 0 0 0 0 Culex restuans 13 0.00 1 0 0.00 0 0 0 0 0 Culex salinarius 0 0 0 0 0 0 0 0 0 0 Culex spp. 377333 94.61 10965846 7.72 377128 96.70 10934 1334 12.20 Culex territans 29 0.01 21 0 0.00 27 0.01 27 1 3.70 Culiseta inornata 1 0.00 1 0 0.00 11 0.00 9 0 0.00 Culiseta melanura 8 0.00 3 0 0.00 1 0.00 1 0 0.00 Culiseta morsitans 0 0 0 0 0 0 0 0 0 0 Culiseta sp. 0 0 0 0 0 0 0 0 0 0 Hippoboscidae 0 0 0 0 0 0 0 0 0 0 Ochlerotatus atropalpus 2 0.00 2 0 0.00 0 0 0 0 0 Ochlerotatus canadensis 26 0.01 8 0 0.00 0 0 0 0 0 Ochlerotatus cantator 0 0 0 0 0 0 0 0 0 0 Ochlerotatus excrucians gr. 0 0 0 0 0 0 0 0 0 0 Ochlerotatus grossbecki 2 0.00 2 0 0.00 0 0 0 0 0 Ochlerotatus japonicus 5958 1.49 1452 11 0.76 0 0 0 0 0 Ochlerotatus sollicitans 4 0.00 4 0 0.00 0 0 0 0 0 Ochlerotatus sp. 0 0 0 0 0 0 0 0 0 0 Ochlerotatus sticticus 7 0.00 3 0 0.00 0 0 0 0 0 Ochlerotatus stimulans 0 0 0 0 0 0 0 0 0 0 Ochlerotatus triseriatus 1066 0.27 443 2 0.45 0 0 0 0 0 Ochlerotatus trivittatus 2066 0.52 183 0 0.00 0 0 0 0 0 Orthopodomyia 0 0 0 0 0 0 0 0 0 0 Orthopodomyia alba 1 0.00 1 0 0.00 1 0.00 1 0 0.00 Orthopodomyia signifera 12 0.00 10 0 0.00 32 0.01 31 0 0.00 Orthopodomyia spp. 1 0.00 1 0 0.00 0 0 0 0 0 Psorophora ciliata 4 0.00 4 0 0.00 1 0.00 1 0 0.00 Psorophora columbiae 35 0.01 19 0 0.00 17 0.00 9 1 11.11 Psorophora cyanescens 0 0 0 0 0 0 0 0 0 0 Psorophora ferox 124 0.03 12 1 8.33 2 0.00 2 0 0.00 Psorophora horrida 0 0 0 0 0 7 0.00 1 0 0.00 Simuliidae 0 0 0 0 0 0 0 0 0 0 Unknownspecies 0 0 0 0 0 0 0 0 0 0 Uranotaenia sapphirina 24 0.01 16 0 0.00 8 0.00 8 0 0.00

287 2006 Species sumspp %spp pools pos %pos Aedes albopictus 2437 0.55 462 1 0.22 Aedes atropalpus 0 0 0 0 0 Aedes canadensis 15 0.00 7 0 0.00 Aedes cinereus 0 0 0 0 0 Aedes intrudens 0 0 0 0 0 Aedes japonicus 9852 2.22 2077 7 0.34 Aedes sollicitans 12 0.00 10 0 0.00 Aedes sp. 1 0.00 1 0 0.00 Aedes stimulans 0 0 0 0 0 Aedes triseriatus 682 0.15 287 2 0.70 Aedes trivittatus 2475 0.56 200 3 1.50 Aedes vexans 4211 0.95 258 1 0.39 Anopheles barberi 4 0.00 3 1 33.3 Anopheles crucians 0 0 0 0 0 Anopheles perplexans 17 0.00 12 0 0.00 Anopheles punctipennis 696 0.16 124 0 0.00 Anopheles quadrimaculatus 76 0.02 35 0 0.00 Anopheles spp. 0 0 0 0 0 Anopheles walkeri 1 0.00 1 0 0.00 Coquillettidia perturbans 219 0.05 38 0 0.00 Culex erraticus 15 0.00 6 0 0.00 Culex pipiens 0 0 0 0 0 Culex restuans 0 0 0 0 0 Culex salinarius 0 0 0 0 0 Culex spp. 423041 95.26 11705 898 7.67 Culex territans 91 0.02 66 0 0.00 Culiseta inornata 0 0 0 0 0 Culiseta melanura 0 0 0 0 0 Culiseta morsitans 0 0 0 0 0 Culiseta sp. 0 0 0 0 0 Hippoboscidae 0 0 0 0 0 Ochlerotatus atropalpus 0 0 0 0 0 Ochlerotatus canadensis 0 0 0 0 0 Ochlerotatus cantator 0 0 0 0 0 Ochlerotatus excrucians gr. 0 0 0 0 0 Ochlerotatus grossbecki 0 0 0 0 0 Ochlerotatus japonicus 0 0 0 0 0 Ochlerotatus sollicitans 0 0 0 0 0 Ochlerotatus sp. 0 0 0 0 0 Ochlerotatus sticticus 0 0 0 0 0 Ochlerotatus stimulans 0 0 0 0 0 Ochlerotatus triseriatus 0 0 0 0 0 Ochlerotatus trivittatus 0 0 0 0 0 Orthopodomyia 0 0 0 0 0 Orthopodomyia alba 0 0 0 0 0 Orthopodomyia signifera 20 0.00 13 0 0.00 Orthopodomyia spp. 0 0 0 0 0 Psorophora ciliata 2 0.00 2 0 0.00 Psorophora columbiae 2 0.00 1 0 0.00 Psorophora cyanescens 0 0 0 0 0 Psorophora ferox 201 0.05 41 0 0.00 Psorophora horrida 1 0.00 1 0 0.00 Simuliidae 0 0 0 0 0 Unknownspecies 0 0 0 0 0 Uranotaenia sapphirina 3 0.00 3 0 0.00

288

Appendix L: Other birds species data, 2002 to 2006

289 Other bird species Count % American Goldfinch 9 3.4 American Kestrel 2 0.7 American Robin 34 12.7 Black-capped Chickadee 1 0.4 Canada Goose 1 0.4 Carolina Chickadee 1 0.4 Carolina Wren 1 0.4 Cedar Waxwing 8 3.0 Chimney Swift 1 0.4 Common Grackle 28 10.4 Cooper's Hawk 1 0.4 Downy Woodpecker 3 1.1 European Starling 4 1.5 Gray Catbird 1 0.4 Great Blue Heron 1 0.4 Great Horned Owl 3 1.1 Hairy Woodpecker 1 0.4 House Finch 16 6.0 House Sparrow 59 22.0 House Wren 1 0.4 Mourning Dove 11 4.1 Northern Bobwhite 1 0.4 Northern Cardinal 34 12.7 Northern Goshawk 1 0.4 Northern Mockingbird 1 0.4 Other Hybrid Duck 2 0.7 Other Species 12 4.5 Ovenbird 1 0.4 Purple Martin 1 0.4 Red-bellied Woodpecker 1 0.4 Red-tailed Hawk 1 0.4 Rock Dove - Columba livia 1 0.4 Song Sparrow 1 0.4 Song Sparrow - Melospiza melodia 1 0.4 Swamp Sparrow 1 0.4 Traill's Flycatcher 1 0.4 Tufted Titmouse 4 1.5 White-breasted Nuthatch 16 6.0 Yellow-shafted Flicker 1 0.4 Total 268

290

Appendix M: Acronym key

291

ADDL - Animal Disease Diagnostic Laboratory

AWC - Available Water Content

C - capsid

CBC - Christmas Bird Counts

CDC - Centers for Disease Control

CDD- cooling degree days cDNA - complimentary DNA

CDPH - Connecticut Department of Public Health

CDPHE - Colorado Department of Public Health and Environment

CFR - case fatality rate

CI - confidence interval

CP - weekly cumulative precipitation

CT - Cycle threshold

CVEC - University of California, Davis, Center for Vector-Borne Diseases

DD - degree days

DL - weekly mean day length

DIM - density of infected mosquitoes

DNA - deoxyribonucleic acid dpi - days post infection

DW - degree week

DyMSiM - Dynamic Mosquito Simulation Model

292 E - Envelope

E - exposed or infected (mosquito or bird)

EID - emerging infectious diseases

EIP - extrinsic incubation period

EIT - extrinsic incubation temperature

Em - exposed (or infected) mosquito

ER - endoplasmic reticulum

GC - gonotrophic cycle

GDD - growing degree days

HDD - heating degree day

I - infectious

Ib - infectious bird

IgG - immunoglobulin G

IgM - immunoglobulin M

Im - infectious mosquitos

IRs - infection rates

IRR - incidence rate ratios

IPM - integrated pest management

JE - Japanese Encephalitis

KUN - Kunjin virus

Lm - larval mosquqito

MADs - mosquito abatement districts

MAGS - Multi-Agent GeoSimulation System

293 MLE - maximum likelihood estimate

MIR - minimum infection rate

MM - mathematical model

MMWR - Morbidity and Mortality Weekly Report mRNA - messenger RNA

NASs - National Audubon Society’s

NDVI - normalized difference vegetation index

NJLT - New Jersey Light Trap

NOAA - National Oceanic and Atmospheric Administration

ODH - Ohio Department of Health

ODRS - Ohio Disease Reporting System

OR - odds ratio

OVP - oviposition

OW - overwinter

PBS - primer binding site

PDI - weekly Palmer Drought Index

PFU - plaque forming units pi - post infection

PORs - Prevalence odds ratios

PRISM - Parameter-elevation Regressions on Independent Slopes Model prM/M - premembrane/membrane qRT-PCR - quantitative real-time RT-PCR

R - recovered

294 RAMP® - Rapid Analyte Measurement Platform

RdRp - RNA dependent RNA polymerase

RNA - ribonucleic acid

RR - relative risk

RT-PCR - reverse transcription polymerase chain reaction

S - susceptible

SAS - Statistical Analysis Software

Sb - susceptible birds

SLE - St. Louis Encephalitis

Sm - susceptible mosquito

SYMVCD - Sacramento Yolo Mosquito and Vector Control District

T - weekly mean temperature

TW - trap week

ULV - ultra low volume

UV - ultra-violet

VBDP – Vector-Borne Disease Program

VIFs - variance inflation factors

WHO - World Health Organization

WNV - West Nile virus

295