A Case-Study Approach to Investigate Transmission, Co-Infection, And

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5-20-2019 A Case-study Approach to Investigate Transmission, Co-infection, and Clinical Sequelae During Epidemics of Dengue and Ebola Virus Disease Jennifer Elizabeth Giovanni [email protected]

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Recommended Citation Giovanni, Jennifer Elizabeth, "A Case-study Approach to Investigate Transmission, Co-infection, and Clinical Sequelae During Epidemics of Dengue and Ebola Virus Disease" (2019). LSU Doctoral Dissertations. 4931. https://digitalcommons.lsu.edu/gradschool_dissertations/4931

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A CASE-STUDY APPROACH TO INVESTIGATE TRANSMISSION, CO-INFECTION, AND CLINICAL SEQUELAE DURING EPIDEMICS OF DENGUE AND EBOLA VIRUS DISEASE

A Dissertation

Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy

The Department of Pathobiological Sciences

by Jennifer Elizabeth Giovanni B.S., Missouri State University, 1994 M.P.H., Emory University, 2001 M.S.N., University of California, Los Angeles, 2011 August 2019

TABLE OF CONTENTS

LIST OF TABLES ...... iv

LIST OF FIGURES ...... vi

LIST OF ACRONYMS AND ABBREVIATIONS ...... vii

ABSTRACT ...... ix

CHAPTER I. INTRODUCTION TO THE RESEARCH AND REVIEW OF THE LITERATURE ...... 1

CHAPTER II. CLINICAL SEVERITY AND IDENTIFICATION OF DIAGNOSTIC DISCRIMINATORS IN PATIENTS WITH DENGUE VIRUS MULTI-SEROTYPE INFECTIONS AND DENGUE VIRUS- LEPTOSPIRA CO-INFECTION, NORTE DE SANTANDER, COLOMBIA ...... 25

CHAPTER III. INDIVIDUAL BEHAVIORS, SOCIAL PRESSURES, AND FAILURES OF EPIDEMIC CONTROL MEASURES THAT CONTRIBUTED TO TRANSMISSION OF ZAIRE EBOLAVIRUS AMONG GEOGRAPHICALLY-DISPERSED FAMILY MEMBERS, LIBERIA ...... 52

CHAPTER IV. CLINICAL SEQUELAE AND FUNCTIONAL DECLINE AMONG SURVIVORS OF EBOLA VIRUS DISEASE, BOMI COUNTY, LIBERIA ...... 73

CHAPTER V. SUMMARY OF RESULTS AND CONTRIBUTIONS TO PUBLIC HEALTH PRACTICE ...... 88

APPENDIX A. HISTORY OF HUMAN EBOLAVIRUS OUTBREAKS, 1976-2014...... 93

APPENDIX B. PUBLISHED REPORTS OF DENGUE VIRUS MULTI-SEROTYPE INFECTIONS, BY COUNTRY AND AUTHOR ...... 94

APPENDIX C. PATIENT-SPECIFIC FUNCTIONAL SCALE ...... 102

APPENDIX D. LSU IRB APPROVAL—DETERMINANT FACTORS OF VIRAL DIVERSITY AND TRANSMISSION ECOLOGY OF DENGUE VIRUS IN COLOMBIA ...... 103

APPENDIX E. COLOMBIA SEROPREVALENCE STUDY ADULT CONSENT AND PEDIATRIC ASSENT FORMS, ENGLISH AND SPANISH ...... 104

APPENDIX F. CENTERS FOR DISEASE CONTROL AND PREVENTION DENGUE CASE REPORT FORM ...... 112

APPENDIX G. ADULT AND PEDIATRIC CLINICAL LABORATORY REFERENCE RANGES ...... 113

APPENDIX H. COLOMBIA SEROPREVALENCE SURVEY SAS CODE ...... 115

APPENDIX I. COLOMBIA SEROPREVALENCE SURVEY RAW DATA ...... 148

APPENDIX J. EBOLA VIRUS DISEASE STUDY IRB APPROVAL ...... 151

APPENDIX K. POST-EBOLA VIRUS DISEASE STUDY CONSENT FORM ...... 152

APPENDIX L. FIELD INVESTIGATION NOTES, LIBERIA, JUNE 2015 ...... 154

APPENDIX M. HISTORY AND PHYSICAL EXAMINATION, HUSBAND, LIBERIA, 2015 ...... 155

VITA ...... 157

iii

LIST OF TABLES

TABLE 1. SEROPREVALENCE SURVEY DESCRIPTIVE DATA FOR DENGUE-POSITIVE AND NEGATIVE PATIENTS ...... 33

TABLE 2. COMPARATIVE FREQUENCIES OF PRESENTING SYMPTOMS, FOR ALL ENROLLEES AND BY DENGUE VIRUS INFECTION STATUS ...... 34

TABLE 3. COMPARATIVE FREQUENCIES OF PRESENTING SYMPTOMS, DENGUE VIRUS SSIS AND MSIS ...... 35

TABLE 4. MEAN HEMATOLOGIC INDICES, BY DENGUE VIRUS INFECTION STATUS ...... 36

TABLE 5. BETWEEN-GROUP DIFFERENCES IN MEAN HEMOGLOBIN ...... 37

TABLE 6. PREDICTIVE MODELS OF LEUKOPENIA AND DENGUE VIRUS SEROTYPE 1 ...... 38

TABLE 7. BETWEEN-GROUP DIFFERENCES IN MEAN PLATELET COUNTS ...... 38

TABLE 8. ASSOCIATION OF THROMBOCYTOPENIA WITH SEROTYPE-SPECIFIC INFECTION AND MULTI-SEROTYPE INFECTION ...... 43

TABLE 9. ADULT MULTI-SEROTYPE INFECTIONS ...... 44

TABLE 10. PEDIATRIC DENGUE VIRUS MULTI-SEROTYPE INFECTIONS ...... 45

TABLE 11. PEDIATRIC MULTI-SEROTYPE INFECTIONS WITH DENGUE VIRUS SEROTYPES 2 AND 4.. 46

TABLE 12. DENGUE VIRUS TRIPLE-SEROTYPE INFECTION ...... 47

TABLE 13. DENGUE VIRUS-LEPTOSPIRA SPP. CO-INFECTIONS ...... 48

TABLE 14. DEMOGRAPHICS AND FREQUENCY OF CLINICAL SYMPTOMS, BY DRY AND WET PHASES OF ILLNESS ...... 60

TABLE 15. EPIDEMIC TRANSMISSION PARAMETER ESTIMATES, EBOLA VIRUS DISEASE FAMILIAL CLUSTER...... 61

TABLE 16. DEMOGRAPHIC CHARACTERISTICS, TRANSMISSION EVENTS, SYMPTOMS, AND OUTCOMES, EVD CLUSTER ...... 67

TABLE 17. ESTIMATIONS OF CASE-SPECIFIC INCUBATION AND INFECTIOUS PERIODS ...... 68

TABLE 18. HISTORY AND PHYSICAL EXAMINATION SUMMARIES, EVD SURVIVORS ...... 75

TABLE 19. FUNCTIONAL ASSESSMENTS USING THE PATIENT-SPECIFIC FUNCTIONAL SCALE ...... 78

LIST OF FIGURES

FIGURE 1. DENGUE VIRUS GENOME ...... 4

FIGURE 2. NUMBER OF PUBLISHED REPORTS OF DENGUE VIRUS MULTI-SEROTYPE INFECTIONS, BY COUNTRY, 1985-2018...... 6

FIGURE 3. PHASES OF CLINICAL DENGUE, WITH CLINICAL SYMPTOMS AND LABORATORY CHANGES ...... 7

FIGURE 4. FILOVIRUS GENOME STRUCTURE ...... 9

FIGURE 5. GEODISTRIBUTION OF HUMAN EBOLAVIRUS OUTBREAKS AND PUTATIVE BAT- RESERVOIR SPECIES, SUB-SAHARAN AFRICA ...... 10

FIGURE 6. VIRAL LOAD KINETICS, DISTRIBUTION OF VIRUS IN BODY FLUIDS, AND ANTIBODY RESPONSE ...... 13

FIGURE 7. REGIONAL MAP OF MEXICO, THE CARIBBEAN, AND NORTHERN LATIN AMERICA ...... 26

FIGURE 8. SEROPREVALENCE STUDY ENROLLMENT FLOWCHART, NORTE DE SANTANDER, COLOMBIA ...... 27

FIGURE 9. PATIENTS WITH DENGUE AND LEPTOSPIROSIS, BY MUNICIPALITY OF RESIDENCE ...... 32

FIGURE 10. MAP OF GUINEA, LIBERIA, AND SIERRA LEONE INTERNATIONAL BORDERS...... 53

FIGURE 11. LOCATION OF ROADSIDE DRUGGIST, BOMI COUNTY, LIBERIA...... 56

FIGURE 12. EVOLUTION OF THE FAMILIAL CLUSTER OF EBOLA VIRUS DISEASE: DISEASE PROGRESSION AND EVENTS ...... 58

FIGURE 13. COUNTY AMBULANCE AND CHLORINE SPRAYER, LIBERIA ...... 63

FIGURE 14. EBOLA VIRUS DISEASE FAMILIAL CLUSTER: TRANSMISSION EVENTS, SYMPTOM ONSET, AND CLINICAL OUTCOMES ...... 66

FIGURE 15. EXAMINATION OF MUSCULOSKELETAL PAIN SITE IN AN EBOLA VIRUS DISEASE SURVIVOR ...... 76

LIST OF ACRONYMS AND ABBREVIATIONS

ADE Antibody-dependent enhancement Ae. Aedes AFI Acute febrile illness aOR Adjusted odds ratio Baso Basophils BDBV Bundibugyo ebolavirus BMI Body mass index BP Blood pressure CDC Centers for Disease Control and Prevention CFR Case fatality ratio cm Centimeter Ct Cycle threshold DENV Dengue virus DENV–1 Dengue virus serotype 1 DENV–2 Dengue virus serotype 2 DENV–3 Dengue virus serotype 3 DENV–4 Dengue virus serotype 4 DHC District health clinic DHN District health nurse DHO District health officer DNA Deoxyribose nucleic acid DPO Days post-illness onset DRC Democratic Republic of the Congo DSI Dual-serotype infection EBOV Zaire ebolavirus ELISA Enzyme-linked immunosorbent assay Eos Eosinophils Eryth Erythrocytes ETU Ebola treatment unit EVD Ebola virus disease HA Headache HBV Hepatitis B virus Hct Hematocrit Hg Hemoglobin HRP Horseradish peroxidase IgM Immunoglobulin M IgG Immunoglobulin G IPC Infection prevention and control IRB Institutional review board kb Kilobases

vii kg Kilogram Leu Leukocyte LSU Louisiana State University MAC–ELISA IgM antibody-capture enzyme-linked immunosorbent assay Mono Monocytes mRNA Messenger RNA MSF Médecins Sans Frontières MSI Multi-serotype infection NHP Non–human primate NIH National Institutes of Health NP Nucleoprotein OD Right eye OR Odds ratio OS Left eye qPCR Quantitative polymerase chain reaction PEVDS Post-Ebola virus disease syndrome PLT Platelets PREVAIL Partnership for Research on Ebola Virus in Liberia RESTV Reston ebolavirus RNA Ribonucleic acid RT–qPCR Reverse transcriptase–quantitative polymerase chain reaction SUDV Sudan ebolavirus SSI Single-serotype infection TAFV Taï Forest ebolavirus US United States VP Viral protein WHO World Health Organization Mean value Median value 𝑥𝑥̅ 𝑥𝑥푥

viii

ABSTRACT

From within their ecologic niches, zoonotic viruses emerge from animal reservoirs into the edges and centers of human habitation to exploit opportunities for unabated transmission within immunologically–naïve populations. Our understanding of where, in whom, and how these viruses emerge is under direct challenge, driving the evolution of modern infectious disease epidemiology within a rapidly-connected global community. The studies presented herein are based on analyses of both aggregate and case-level data, which, we argue, provide unique insight into the complexities of transmission, co-infection, and clinical sequelae occurring within, and arising from, epidemics of emerging zoonotic viruses. In Chapter II, we investigate differences in disease severity between single- and multi-serotype dengue virus infections, and examine the clinical presentation of patients with dengue versus patients with dengue virus-Leptospira co-infection. Our objective was to construct a diagnostic algorithm to aid clinicians in the early recognition and treatment of patients with multi- pathogen infections. In Chapter III, we reconstruct a six-person transmission chain of Ebola virus disease in Liberia. We analyze the individual behaviors and epidemic control measures that contributed to, and interrupted, transmission. Finally, in Chapter IV, we present a case series of pain profiles and functional disability among survivors of Ebola virus disease, a contribution to the newly- recognized post-Ebola virus disease syndrome.

Our research contributes three principal implications for clinical and public health practice. In Norte de Santander, Colombia, we provide the first prevalence estimates of dengue virus multi-serotype infection and dengue virus-Leptospira co-infection. Identifying the reservoirs of pathogenic Leptospira spp. in Los Patios, and initiating public and clinical education campaigns, will be important interventions to mitigate environmental persistence, prevent additional cases, and encourage early initiation of antibiotic therapy. From case investigations in Liberia, we identify sweat as an underrecognized driver of the Ebola virus disease epidemic, and discuss the importance of social distancing to interrupt transmission. Finally, we describe the clinical symptoms and longitudinal disability experience of three Ebola virus disease survivors to contribute to the growing evidence of a post-viral syndrome in survivors.

CHAPTER I INTRODUCTION TO THE RESEARCH AND REVIEW OF THE LITERATURE

Approximately 75% of new diseases emerge from animals.1 At the human-animal interface, a complex set of eco-evolutionary pressures induces changes in a virus’ genome, and, therefore, the RNA or DNA architecture. These architectural modifications enable expansion of the viral host range2, and confer transmission and replicative advantages within immunologically-naïve populations.3 Genomic changes are the result of interdependent evolutionary pressures from diverse ecosystems, physiologic processes, population changes, social dynamics, and human behavior.4 Understanding the confluence of these pressures informs changes in the global risk surface,5 that is geographies with conditions, pathogens, and ecologies rife for emergence events. The Institute of Medicine developed the Convergence Model to characterize four principle categories of evolutionary pressures that drive emergence events.6

1) Genetic and Biological Factors Genome mutation, recombination, and reassortment optimize routes of transmission and exploit pathways of suppression and/or avoidance of the host immune response.7-9 Particularly for error- prone RNA viruses, these changes may encode a new protein or receptor, expanding the host range of the pathogen, such as the human adaptation of HIV from non-human primates (NHPs) to human,10 pandemic influenza H1N1 from birds,11 and Plasmodium falciparum from gorillas.12

2) Physical and Environmental Factors Deforestation and road construction create threads for human travel through previously impervious forests, providing opportunities for spillover from animal reservoir to human host.13 The repurposing of land for large-scale agricultural production, particularly for palm oil production, gives rise to food insecurity by removing food-source species habitats, driving changes in hunting practices and consumption of NHPs,13,14 which share genetic similarities with humans.7

3) Ecological Factors Rising atmospheric temperatures increase the amount of rainfall, which provides pools of standing water for mosquitos breeding sites, and changes the density and diversity of vegetation and resident species.15 Warmer temperatures and higher humidity permit expansion of mosquito vectors into new regions;16 increase biting rates, which ultimately increases the risk to humans of mosquito- borne viral and parasitic infections.

4) Social, Political, and Economic Factors Urbanization, mining, political instability, and environmental degradation create conditions of poverty, social inequality,17 and human susceptibility to emerging viruses.6 The absence, or decline, of public health infrastructure in the developing world has created regions in which the detection and control of an emergence event is weakened or nonexistent,18 such as the West Africa Ebola epidemic and the ongoing outbreak in war-torn countries of Central Africa.

INTRODUCTION TO THE RESEARCH AND RATIONALE FOR THE USE OF CASE-STUDY METHODOLOGY

Shoe leather epidemiology has been replaced by the use of mathematical models for large-scale predications for epidemic response.19 These models necessarily rely upon the accuracy and amount of data used to inform model parameters that attempt to identify and explain causal links and pathways.5 The ability to detect and predict emergence events requires targeted surveillance among susceptible populations, combined with the ability to rapidly deploy a robust, appropriate, and adaptive public health response. A multidisciplinary engagement of basic science, medicine, social science, and policy is required to address transnational health issues and mitigate the consequences of epidemics. Working within these complexities requires integration of empirical evidence, the underlying mechanisms of transmission, clinical management, and preventive strategies for global public health practice.

The case study, or naturalistic approach, has a long tradition in clinical practice, medical research, and infectious disease epidemiology. The central tenet of a case study is a need for in-depth, multi-faceted explorations of complex scientific and anthropologic phenomena within their natural context.20 In contrast to the experimental design, in which predictor variables are manipulated to explore associations with an outcome of interest, the case study approach is based in interrogation, an approach that submits to examination of the critical events, interventions, policies, and consequential human and pathogen responses. The case study approach lends itself, with limitations, to in-depth analysis of complex events and associations when human behavior is an essential—and under-appreciated— influence on pathogen behavior. The case study affords granular insight into gaps in control strategies, microscale drivers of transmission, and cultural factors that determine the success or failure of transmission control strategies.

ORGANIZATION, HYPOTHESIS, AND RESEARCH OBJECTIVES

DENV is the most common arbovirus infection, increasing in both its global distribution and profound burden of disease, primarily due to the co-circulation of the four serotypes.21 EBOV, once said to be a ‘bit player in the global drama of emerging diseases’,22 has demonstrated its ability to cause widespread morbidity and mortality, and its potential for regional destabilization and interruptions to global transportation and commerce. The foci of this research are dengue virus and EBOV, identified by the World Health Organization (WHO) as two of ten greatest threats to global health in 2019.23 We present investigations into the epidemic transmission, co-infection, severity, and clinical sequelae of hyper-endemic circulation of dengue viruses (DENV) in northeastern Colombia and the 2014 emergence of Zaire ebolavirus (EBOV) in West Africa.

The overarching hypothesis of these studies is that case-study methodology is uniquely able to: 1) Identify changes in clinical severity related to multi-pathogen infections; 2) Describe the contextual drivers of viral transmission and estimate key transmission parameters for use in predictive modeling; and

3) Characterize post-viral symptomology and disability after infection with an emerging zoonotic virus.

Chapter II. Dengue Virus Multi-Serotype Infection and Leptospira Co-Infection

We performed a hospital-based serosurvey of patients with acute febrile illness (AFI) in Norte de Santander, Colombia during the epidemic years of 2013 and 2014.

Research Objectives 1) To estimate the prevalence of DENV multi-serotype infection (MSI) and DENV- Leptospira co-infection in Norte de Santander. 2) To examine differences in clinical severity and identify indicators associated with differential clinical presentation among patients with DENV MSI compared to patients with DENV single-serotype infection (SSI), and patients with DENV-Leptospira co- infected compared to patients with DENV only. 3) To develop a diagnostic algorithm for use by clinicians in DENV-endemic areas lacking diagnostic capabilities.

Chapter III. Familial Transmission of Zaire ebolavirus

Using an instrumental case study approach,24 we investigated the transmission events, progression of EVD, and the clinical outcomes of a six-person cluster in Liberia.

Research Objectives 1) To describe the individual behaviors, settings, modes and routes of EBOV transmission, highlighting the circumstances of single-day exposure and transmission events. 2) To estimate, with greater precision, the key transmission parameters of EBOV using individual exposure-transmission data. 3) To illustrate protective measures used by the family and local health workers to prevent community transmission, whilst describing deficiencies in the international response.

Chapter IV. Post-Ebola Virus Disease Syndrome

To investigate physical complaints and associated disability among survivors of the EVD cluster described in Chapter III, we conducted serial physical examinations and measured changes in functional ability by applying intrinsic case study methodology24 specific to the exploration of unique phenomena.

Research Objectives 1) To perform a comprehensive history and physical examination. 2) To identify and examine the anatomical locations of pain. 3) To measure functional ability between pre-EVD and at two post-EVD timepoints.

REVIEW OF THE LITERATURE—DENGUE VIRUS

Dengue is a complex viral illness transmitted among humans by Aedes mosquitoes. It is one of 53 human-pathogenic flaviviruses, which include Japanese Encephalitis virus (JEV), West Nile virus (WNV), Yellow fever virus (YFV), and Zika virus (ZIKV), each of which have historical or emerging global distribution.25,26 First identified during WWII in Southeast Asia among military personnel,21 its emergence and persistence have made it one of the leading causes of tropical morbidities.27 There have been episodic outbreaks and epidemics since the 18th century.28 As climate change alters weather patterns and increases ambient temperatures, rainy seasons are lengthening, and the mosquito vector is expanding from its tropical regions to temperate climates with naïve populations. Between 1990 and 2013, the incidence of dengue doubled each decade, from 8.3 million apparent cases in 1990 to 58.4 million in 2013.27 In 2010 alone, the total global burden of dengue was estimated at 390 million cases, of which more than 75% (294 million) cases were undiagnosed and untreated.29 In 2019, nearly 40% of the world’s population is at risk of infection with a dengue virus, a disease with a mortality rate of 20%.23

Virology

DENV (family Flaviviridae, genus Flavivirus)30 is a positive-sense, single-stranded RNA virus of 11- kilobases (kb). During translation, the genome is cleaved into ten proteins to yield three structural proteins and seven non-structural proteins (Figure 1).31 E, on the virion surface, is the principle inducer of antigenicity and thereby the primary target for the human antibody response.32 E also forms the basis of classification of the four DENV serotypes (DENV-1-4), which share ~70% sequence homology but differ at the E protein in sequence and structural translation.33 The homology of the sequences and similarity in protein structure form the basis of temporary cross-protection of antibodies against one DENV serotype to infection of another serotype and to other Flaviviridae.34

Figure 1. Dengue Virus Genome The viral particle is encoded by structural proteins C (capsid), E (envelope), and M (membrane); nonstructural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, NS5) control replication. Reprinted from Guzman MG. Lancet. 2014;385(9966):453-465.

Epidemiology

DENV has both a sylvatic transmission cycle and a human transmission cycle. While considered to be of the same evolutionary origin, the two cycles are now maintained separately in different ecologies, with different strains, and with different mosquito species.35 Sylvatic dengue transmission occurs

among forest-dwelling non-human primates (NHPs) and Aedes luteocephalis and furcifer in Sub- Saharan Africa, and Ae. niveus spp. in Southeast Asia.35 The human transmission cycle is maintained primarily by the anthropophilic Ae. aegypti, which utilize water-storage containers for oviposition and remain inside dwellings for frequent blood meal acquisition.36 Their introduction to the Neotropics during the Age of Sail likely occurred by slave and commerce vessels traveling from West Africa to the Caribbean.37,38 During the construction of the Panama Canal from 1903-1914, the morbidity and mortality from YFV threatened progress of its construction.25 Public health efforts focused on eradicating the mosquito vector, and transmission of the virus ended in 1906. However, when eradication efforts failed in Cuba, Venezuela, and the Caribbean Community, the species returned. Today, Ae. aegypti are endemic throughout the tropics, with climate change driving their expansion into temperate regions.15

Multi-Serotype Infection A mosquito acquires infection with more than one DENV serotype by acquisition of a single blood meal from a human with an MSI, or multiple blood meals from differentially-infected humans.39-42 Human MSI results from one or more bites by a mosquito infected with multiple DENV serotypes, or by consecutive bites from differentially-infected mosquitoes.43,44 The first report of a DENV MSI occurred in Puerto Rico in 1982.45 Since then, MSIs are reported with increasingly frequency and of and expanding global distribution (Figure 2, Appendix B), with India and Brazil reporting the majority of cases. The scholarship of the clinical implication of DENV MSI is sparse; some studies report and describe the clinical severity of MSI patients, while others wholly exclude them from analyses and provide no clinical data.

Among case reports of MSIs and studies of MSIs as the primary outcome,46,47 the evidence is inconclusive. In one study of MSIs in a Malaysian cohort,46 DENV-1/2 and DENV–1/3 combinations were associated with at least one severe dengue manifestation. Patients with DENV- 1/2 had severe thrombocytopenia (<50.0 x 109 platelets/L), pleural effusion, and fluid accumulation with respiratory distress. Hemorrhagic manifestations have been observed with greater frequency in patients with MSIs (Appendix B).47,48 MSIs will be detected with increasing frequency, and understanding how they modulate disease and contribute to the modern epidemiology of dengue will be important for clinical care and public health surveillance programs.

Figure 2. Number of Published Reports of Dengue Virus Multi-Serotype Infections, By Country, 1985-2018 Corresponding references are listed in Appendix B by country and author’s last name.

Human Infection and Disease

Dengue is a systemic and dynamic disease with a clinical spectrum encompassing asymptomatic infection to a critical illness.49 Its severity is influenced by host genetics, pre–existing immune repertoires, and nutritional factors.31 Following an incubation period of three to seven days, the course of illness follows three phases—febrile, critical, and recovery (Figure 3).49

Figure 3. Phases of Clinical Dengue, with Clinical Symptoms and Laboratory Changes Reprinted from WHO. Dengue: guidelines for diagnosis, treatment, prevention and control: new edition. 2009.

Febrile Phase The initial symptom of dengue is characterized by a temperature ≥38.5°C and then sudden onset of headache, myalgia, and arthralgia.49 The patient will develop thrombocytopenia, leukopenia, and elevations in hepatic transaminases.

Critical Phase Between days post-illness onset (DPO) 4-7, the stable patient can deteriorate suddenly to the critical phase is marked by evidence of endothelial compromise (i.e., increasing hematocrit, lethargy, abdominal pain, hypotension, rapid pulse), severe thrombocytopenia, and diffuse ecchymoses.49 These signs and symptoms of hemodynamic collapse precede cardiopulmonary shock and death.

Not all patients with dengue progress to the severe forms of disease; however, early diagnosis of DENV infection enables identification of at-risk patients, and is critically important for initiation of supportive therapies.49

Molecular detection of DENV RNA is the gold standard for laboratory diagnosis of acute dengue, but, for the majority of affected countries, not available. Therefore, diagnosis is symptom-based, although rapid antibody-detection kits increasing available, largely through WHO initiatives to expand laboratory capacity in developing countries.18

Recovery Phase Recovery begins when IgM and IgG concentrations approach their peak (Figure 3). Endothelial compromise is reversed, and symptoms quickly resolve. A profound fatigue lasting up to a month has been reported in up to 50% of patients,50 and has been associated with age and female sex.51,52

Immune Response to DENV Infection Disease progression and outcome are determined by the balance of the protective and pathologic immune responses (i.e., suppression of viral invasion) (i.e., pro-inflammatory).53 In the primary immune response, the immune system has its first encounter with a DENV serotype. Production of IgM antibodies produced by plasma cells and memory B-cells neutralizes the virus, which is cleared by the lymphatic system.31,54 Serotype-specific IgG antibody confers life-long immunity to infection by the same serotype, with temporary cross-protection against infection by a heterologous serotype.55

A unique aspect of dengue is the potential to have up to four infections with each of the four serotypes during a lifetime. The primary DENV infection is the first encounter of the immune system with the virus, producing IgM and serotype-specific IgG antibodies.31,54 Recovery occurs when the virus is neutralized and removed by the lymphatic system. Secondary infection, however, has the potential to invoke a more complicated immune response, and, therefore, a critical clinical progression of disease. Sequential infection by one of the four serotypes is the immunopathology of the critical phase of dengue. Referred to as original antigenic sin,34 and antibody dependent enhancement (ADE),56 this critical state is characterized by systemic, excessive immune activation of pro–inflammatory cytokines produced in response to a second infecting serotype. Due to their sequence homology and conformational similarities at the E receptor site, pre-existing antibodies produced during the patient’s primary infection are stimulated by a secondary serotype.56 Rather than mount a new, energy- expensive, highly-specific immune response, expression of viral epitopes activates pre-existing memory B- and T-cells. 56 Cross-protection leads to cross-induction, resulting in suboptimal binding avidity and only partial neutralization of the secondary, heterologous serotype. Incomplete neutralization of viral replication results in excessive cytokine release, systemic inflammation, endothelial disruption, and hemodynamic instability.34,57

REVIEW OF THE LITERATURE—ZAIRE EBOLAVIRUS

The ebolaviruses (Filoviridae) are enveloped, non–segmented, negative–stranded RNA viruses.58 Paleoviral dating places their existence on Earth since the early Miocene period,59 approximately 23.8 to 5.3 million years ago.60 There are six recognized species61: Zaire ebolavirus (EBOV), Sudan ebolavirus (SUDV), Taï Forest ebolavirus (TAFV), Bundibugyo ebolavirus (BDBV), Reston ebolavirus (RESTV),

and Bombali ebolavirus.62,63 At present, EBOV, SUDV, TAFV, and BDBOV are known to be pathogenic in humans. In the Philippines, where RESTV has been found in bats colonies,64 the virus caused outbreaks in laboratory NHPs65,66 and domestic swine.67 In both outbreaks, animal handles developed antibody evidence of exposure to the virus, but none developed clinical symptoms.68,69 The newly-identified Bombali ebolavirus can mediate entry into human cells but has not been associated with human disease.63

Figure 4. Filovirus Genome Structure Reprinted from Messaoudi, I. Nat Rev Microbiol. 2015;13:663–676

Virology

The EBOV genome is seven linearly-arranged genes of 16-19 kb (Figure 4).58 Nucleoprotein (NP) forms the helical filaments that encapsidate the viral RNA and confer protection from nucleases and products of the innate immune response. 58 Viral proteins (VP) 35 and 30 are transcription activation factors and interferon antagonists and suppressors of innate immune-cell signaling.70 Soluble glycoprotein (sGP) circulates at high levels early in infection and is thought to impair the humoral immune response by binding and steric impedance of neutralizing antibodies.71 VP40 is required for assembly and budding of viral particles from infected cells, and is the most abundant protein in the virion.72 VP24 is an interferon antagonist, and, L, the RNA-directed RNA polymerase responsible for synthesis of positive-sense RNA and transcription of viral mRNA.58

Ecology

The fruit bat Rousettus aegyptiacus is a recognized reservoir of Marburg virus, a filovirus progenitor of the ebolaviruses.73,74 Historically, Central African outbreaks of ebolavirus have been linked to human contact and consumption of bats and sick or dead NHPs (Appendix A) within remote, densely- forested regions in Central Africa (Figure 5) often associated with mining activity.75 In areas where NHPs are rare or absent, fruit bats serve as an alternative protein source.76,77

In perhaps the germinal paper of the 2013-2016 West Africa EBOV outbreak, a two-year-old boy, known to play inside a large tree stump that hosted a bat roost, was identified as the index case of the West Africa epidemic.78 In 2018, a partial sequence of EBOV was identified from a Greater Long- fingered bat in Nimba County, Liberia,79 an area with mining activity.80 In the same year, the full genome of Bombali ebolavirus was isolated from an Angolan freetail bat colony in the Bombali District of Sierra Leone,63 also a mining region.80 Given these recent findings, the long-standing hypothesis implicating Chiroptera bat species as reservoirs of filoviruses is gaining traction.73,81,82

Figure 5. Geodistribution of Human Ebolavirus Outbreaks and Putative Bat-Reservoir Species, Sub- Saharan Africa Reprinted from Spengler JR. Emerg Infect Dis. 2016;22(6):956-963

Epidemiology

Emergence History of the Ebolaviruses The first recognized spillover of an ebolavirus, SUDV, into humans occurred in June 1976 in present- day Republic of South Sudan in a cotton factory worker who had consumed bushmeat.83 The second spillover event, also of SUDV, occurred in August 1978, near the Ebola River in DRC, in a man who had consumed bushmeat.84 He attended a local health clinic and received an injection. The contaminated needle was subsequently used to administer injections to other patients at the clinic, which infected 318 people (CFR 88%). In 1979, an outbreak of SUDV among 34 people (CFR 65%) occurred in the same region of the Republic of South Sudan as the 1976 outbreak.85 Following a quiescence of 15 years, TFV was isolated in 1994 from an ethnologist working in the Taï Forest in Côte d’Ivoire who received a percutaneous needle stick while performing a necropsy of a chimpanzee.86 In 1994, EBOV was identified as the causative ebolavirus species of three separate outbreaks in Gabon (n=52, CFR 60%), each linked to alterations in the forest canopy by mining activity, gorilla mortality, and the butchering and consumption of a sick chimpanzee.87 From this same outbreak, a Gabonese

HCW became infected, was transferred to South Africa, and infected a South African HCW.88 In 1995, a charcoal maker in DRC was the index case of an outbreak of EBOV that infected 315 people (CFR 81%).89 Between 2000 and 2012, there were ten outbreaks in Uganda, Republic of Congo, DRC, and the Republic of South Sudan (Appendix A). During this period, the newest ebolavirus species, BDBV, emerged in Uganda in 200790-92 and DRC in 2012.93

Epidemic Parameters The incubation period for a pathogen, defined as the interval from exposure to onset of clinical symptoms, is a function of the intensity and nature of contact.94 Therefore, the incubation period is dependent on the infectious dose (e.g., viral load), route of infection (e.g., percutaneous, inhalational, contact), and the duration of exposure (i.e., single contact or prolonged exposure periods). For outbreaks of ebolavirus, the incubation period is a key epidemic parameter used for determining the timing and length of isolation and quarantine, length of enhanced syndromic surveillance, and for declaring the end of an outbreak.19,95 The current incubation interval used for epidemic monitoring is 2-21 days, which is derived from the first outbreak of SUDV in 1976.96,97 Across all species and modes of transmission, the mean incubation period is 3.4-12.7 days (range 1-21 days).98 By species, the incubation period for EBOV is 5.3-12.7 days (range 1-21 days),19 3.35-12 days (range 1-16 days) for SUDV,19 and 6.3-7 days (range 2-20 days) for BDBV.91,92 For single-day exposures, which provide greater precision but are rarely documented, the mean incubation period is 6.22 days (SD ± 1.57 days) across all species.95 Needle-stick transmission had a mean incubation period of 6.3 days, compared to 9.5 days for contact with blood and bodily fluids.89,97,99-101

The Changing Epidemiology of Ebolavirus Outbreaks In December 2013, a two-year-old boy in Guinea set off an historic outbreak in West Africa that infected at least 28,616 people (CFR 39.5%).78 For the first time in history, an ebolavirus outbreak had broached a highly-populated urban center. Then, in 2017, the DRC experienced its eighth outbreak of EVD when the index case butchered the wild boar at the same market where an NHP was butchered and sold.103 In 2018, two separate outbreaks the DRC would herald a watershed event for outbreaks of ebolavirus: transmission in an active war zone.104 Today, transmission continues unabated, with no evidence of slowing transmission or decreases in cases.

Human Ebola Virus Disease

Sources and Modes of Transmission Ebolavirus transmission to humans occurs by contact with the blood, body fluid, or infectious carcass of a NHP, bat, antelope, and other forest mammals.73,105 Human-to-human transmission occurs by contact with infectious body fluids of a living or deceased human. Historically, an index case is a male member of a remote forested community who works in mining or logging, or who recently hunted and butchered an infected animal (Appendix A). An index case sets in motion an outbreak through two primary transmission pathways. In the first transmission pathway, female family members, who serve as primary caregivers in traditional African cultures, provide bedside care for the index case. Stool and vomitus are the primary sources of transmission during outbreaks, as high viremia and viral

shedding through fluid loss coincide with the phase of illness when patients require care.106 Additional secondary cases arise among community members who travel to provide care, attend a religious ceremony, or participate in funereal rituals. In the second transmission pathway, HCWs who are infected when the index case seeks care at a local or regional facility. For smaller, localized outbreaks, an index case infects an HCW at a rural facility, who generates tertiary cases among family members and a smaller number of patients who reside in the surrounding village. For larger outbreaks, an index case infects an HCW at a larger health facility, which serves a large population center. Tertiary transmission occurs within the hospital among patients and family members, who return to distant villages and homes within larger cities. Percutaneous exposures (i.e., needle-stick, scalpel injury, injection using a contaminated needle) are associated with a high infectious dose and high mortality. Among patients exposed by use of contaminated needles during the 1976 outbreak, the case fatality rate was 100%, versus 80% among patients exposed to infectious body fluids.107

EBOV has been detected in saliva, sweat, urine, breast milk, synovial fluid, spinal fluid, ocular fluid, stool, vomitus, and semen.108 The contribution of saliva, sweat, urine, breast milk, and products of conception to transmission during an outbreak is unknown.109 There are no documented reports of human transmission from aerosols, but the role of aerosols110 and virus persistence on fomites has been suggested.111-113

Until the West Africa outbreak, human transmission was considered restricted to the symptomatic period of EVD, and the risk of transmission following recovery was, theoretically, zero.109 However, in July 2015, two separate chains of transmission, one in Liberia114,115 and one in Guinea,116 were linked by molecular analyses to sexual intercourse with male survivors whose semen was EBOV- positive. It is now recognized that harborage of virus in immune-privileged sites is an ongoing risk for potential outbreaks in the region.

Clinical Progression The latent period, or dry phase, is symptomatic onset fever, headache, myalgia, arthralgia, sore throat, and a distinguishingly profound weakness.117 In the first three days post-illness onset (DPO), the viral load increases exponentially in blood, from undetectable to >105 viral particles/mL, and continues within the vascular system for another 3-7 days.118 The infectious period, or wet phase, begins with onset of vomiting and diarrhea and shedding of virus through fluid loss. It corresponds to peak viral loads, and, for some, progression to hemorrhage and fatal sequelae. Pregnancies will spontaneously abort during this phase.119 Diaphoresis is remarkable; extensive fluid loss from vomiting and per- rectum peristaltic expulsion is estimated at 3-10 L per day.117 If present, hemorrhage appears first at the gum line at intravenous catheter and injection sites, with progression to subconjunctival hemorrhage, melena, petechiae, and diffuse ecchymoses.120 Loss of electrolytes, most critically potassium and calcium, causes muscular rigidity, confusion, alterations in consciousness, cardiac dysrhythmias, hypovolemia, and vascular collapse.106 In the absence of timely, supportive care, organ failure and death occur within 10-16 DPO.

Disease Outcomes Viral load is a predictor of clinical severity and outcome.121 The West Africa outbreak provided a large dataset from which analyses of prospective and retrospective associations could be investigated. From these data, a clearly-defined, divergent course for survivors and decedents emerged based on the differences in the amount and kinetics of their viral loads.121-128 Patients who survive EVD have slower viral replication, lower peak viral loads (Figure 6), and a shorter duration of less severe symptoms.117 In contrast, patients who succumb to EVD have higher viral loads and demonstrate rapid increases and a prolonged symptomatic period of increasing severity.117 In an analysis of viral loads in 65 patients in Sierra Leone, patients who presented with fewer than <100,000 EBOV copies/mL of serum had a CFR of 33%, whereas those with a viral load of >10 million EBOV copies/mL of serum had a CFR of 94%.128 Importantly, patients with higher viral loads have rapid onset of severe, debilitating symptoms. As patients become bedridden, requiring total care by family and community members, prolonged contact with highly-infectious body fluids dramatically increases the risk of infection for caregivers.

Figure 6. Viral Load Kinetics, Distribution of Virus in Body Fluids, and Antibody Response Reprinted from Malvey D. Lancet. 2019;393:936-948.

Immune Response While surviving EVD is multifactorial, the governing principles is inactivation of replicating virus by the humoral immune response.118,123 The natural portals of virus entry are the mucosal surfaces and the skin, where macrophages and dendritic cells, the primary cellular targets of ebolaviruses, promote dissemination to multiple cell types throughout the body fluids and tissues,58 including sites previously considered immune-privileged (e.g., central nervous system [CNS],129 ocular chamber130).

Severe EVD is characterized by an intense inflammatory response with by high concentrations of pro- inflammatory mediators.131 Simultaneous immune suppression and overactivation results in global immunosuppression, a complex pathogenesis of insufficient and deranged innate and adaptive immune responses.132,133 Systemic and uncontrolled viral replication causes apoptosis, tissue necrosis, coagulopathies, and compromise of the vascular endothelia.134-136

There is no licensed treatment for EVD. Survival is achieved by rapid and balanced humoral and cellular immune responses.137 Aggressive rehydration with concomitant electrolyte replacement can support cell and tissue function until development of the antiviral immune response.131 In Africa, treatment for EVD is limited to oral rehydration salts, oral nutrition, intravenous fluid recessitation, paracetamol, and presumptive treatment for malaria.138 In the absence of such therapies, multiple organ failure and death occur within approximately 10 days of symptom onset in humans.

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CHAPTER II CLINICAL SEVERITY AND IDENTIFICATION OF DIAGNOSTIC DISCRIMINATORS IN PATIENTS WITH DENGUE VIRUS MULTI-SEROTYPE INFECTIONS AND DENGUE VIRUS-LEPTOSPIRA CO-INFECTION, NORTE DE SANTANDER, COLOMBIA

The clinical presentation of dengue as a non-specific AFI is indistinguishable from other tropical etiologies, which constitute an already heavy burden of disease in endemic areas. In the tropics, viruses, bacteria, and parasites occupy similar ecological niches, and infection is not restricted to one pathogen. For regions of hyperendemic circulation of all four DENV serotypes, such as Colombia, concurrent infection with more than one serotype, or MSI, occurs from the bite of a single mosquito infected with multiple serotypes,1,2 sequential mosquito bites from differentially-infected mosquitoes, as the presence of two DENV serotypes in one mosquito has been demonstrated.3,4 as has simultaneous infection with two arboviruses.5 Since 2003, Colombia has been hyperendemic for co-circulation of all four serotypes,6 yet there are no published reports of MSIs in Colombia.

Leptospirosis is a bacterial disease caused by one or more of 250 pathogenic leptospire serovars.7 The burden of disease is known to be high but is undefined.8 Untreated disease can lead to kidney and liver failure, with death in 5-15% of cases.9 The bacteria are responsive to treatment with readily available, cost-effective antibiotics, including doxycycline; therefore, early diagnosis is critical to improving patient outcomes.10 Because of overlapping geographies and the similarity of acute presentation, leptospirosis is often misdiagnosed as dengue.11,12 In Colombia, the seroprevalence of anti-Leptospira antibodies is as high as 76%.13,14 Among 53 patients in Córdoba given the diagnosis of dengue, more than half were retrospectively reclassified as cases of leptospirosis.15 As a high- consequence pathogen,16 these findings highlight the global need for clinical education, laboratory diagnostic capacity, and the early for the accurately diagnose and treat disease.

Despite similarities in their clinical presentation, dengue, an intracellular virus,17 and the pathogenic leptospires, which are extracellular bacteria,18 differ in their underlying pathophysiology, such that divergent host responses, including clinical presentation, may have utility in developing clinical algorithms to discriminate differences in their laboratory profiles. We hypothesized that clinical and laboratory indicators exist to differentiate between these infections, and we therefore aimed to develop a diagnostic algorithm for use in clinical settings.

MATERIALS AND METHODS

Population Screening and Enrollment

The State of Norte de Santander (Figure 7) lies within the northeast region of Colombia along a common border with Venezuela. The region hosts the Pan American Highway, an artery of vehicular and commercial traffic connecting the Caribbean, Venezuela, and internal regions of northern Latin America.19 The capital city, Cúcuta, hosts a border-crossing of the Pan American Highway and was the 2003 location of DENV-3 reintroduction from Venezuela to Colombia.20

Norte de Santander, Colombia

Figure 7. Regional Map of Mexico, the Caribbean, and Northern Latin America

As part of a larger study of serotype diversity and transmission ecology in Norte de Santander, we conducted a cross-sectional seroprevalence study of DENV and pathogenic Leptospira among patients with an AFI presenting for care at Hospital Erasmo Meoz, a 700-bed referral and teaching hospital in Cúcuta, and the Hospital de Los Patios, a multi-service referral center in the Cúcuta suburb of Los Patios. An AFI was defined as a fever >38.0°C of unknown etiology. Continuous enrollment occurred from January 2013 to October 2014, which coincided with an epidemic of dengue in Colombia.21 Adults (>18 years) who agreed to participate in the study provided written informed consent for use of clinical and laboratory data, and use of a serum aliquot for research testing at Louisiana State University (LSU). Children (7-17 years of age) provided verbal informed assent, with written informed consent from a parent or guardian; informed consent was provided by a parent or guardian for children <7 years of age.

Patients were interviewed for history of present illness. Clinical and laboratory data were extracted from medical records. De-identified data were transmitted electronically to LSU for analysis. Serum aliquots were maintained at -80o C by study personnel. Temperature integrity was monitored and maintained during international shipping to LSU.

Figure 8. Seroprevalence Study Enrollment Flowchart, Norte de Santander, Colombia Abbreviations: DENV, dengue virus; N, total number of enrolled patients; n, number of patients per group. Numbers within parentheses correspond to percentages (%).

Ethics Statement The study protocol was approved by the Instituto Departamental de Salud of Norte de Santander, and ethics review boards at the Hospital de Los Patios, the Hospital Erasmo Meoz, and the LSU Human Subjects Institutional Review Board (IRB) (Appendix D). Serum aliquots were obtained in compliance with US and Colombian regulations for the use of human subjects in clinical research.

Laboratory Methodology

Detection of Dengue Virus RNA Molecular testing for DENV serotypes 1-4 RNA was performed at the LSU School of Veterinary Medicine, Department of Pathobiological Sciences, by multiplex RT-qPCR targeting the DENV- specific NS1 gene, as previously described.22 RNA was extracted using the MagMaxTM-96 Viral RNA Isolation Kit (Ambion/Life Technologies, Carlsbad, CA) and detected by RT-qPCR with the Superscripts III® Platinum® One-Step RT-qPCR system (Life Technologies, Carlsbad, CA) on the LightCycler 480 (Roche Diagnostics Corp., Indianapolis, IN). Extracted samples were tested using the following protocol: (1) reverse transcriptase step (1 cycle) at 48°C for 2 minutes and 95°C for 2

minutes; (2) amplification and data recording step (40 cycles) at 95°C for 15 seconds and 60°C for 30 seconds. Primers and probes specific to each DENV serotype23 were designed and obtained through Integrated DNA Technologies, Inc. (Coralville, IA).

Detection of Pathogenic Leptospira spp. DNA Detection of the pathogenic gene LipL32 was performed according to the protocol developed by Stoddard, et al.24 Leptospira DNA was extracted using the MagMaxTM-96 DNA Multi-Sample Kit (Ambion/Life Technologies, Carlsbad, CA). The reference standard for LipL32 was Leptospira interrogans serovar icterohaemorrhagiae from culture-extracted DNA (Dr. Sreekumari Rajeev, University of Georgia, College of Veterinary Medicine, Veterinary Diagnostic Laboratory). The primers and the probe were obtained from Integrated DNA Technologies according to the sequences used by Stoddard, et al.24 The amplification protocol (LightCycler 480, Roche Diagnostics Corporation, Indianapolis, IN) consisted of 8 minutes at 95 °C, by 45 cycles of amplification (95 °C for 10 seconds and 60 °C for 30 seconds), finishing with a cool cycle of 45 °C for 5 minutes. Serial dilutions of cDNA, probes, and primers were performed to achieve a desired concentration approximating the expected concentration of the target gene in human sera.24

Measurement of Anti-Dengue Virus IgM and IgG Antibody Concentrations Anti-DENV IgM and IgG was detected by MAC-ELISA (InBios International, Inc., Seattle, WA). The assay was optimized for each DENV serotype as previously described.22 Briefly, 96-well plates were coated with 1 g/mL of Flavivirus capture antibody 4G2 at 4°C overnight, blocked for 1 hour with 5% dry milk in PBS at 37°C, and incubated with each of the serotypes or uninfected cell culture (media control plate)μ for 2 hours at 37°C. Diluted human serum (100 µL/well) was incubated for 2 hours at 37°C on a shaker. Plates were washed 3 times with PBS and 0.1% Tween, and then incubated at 37°C for 1 hour with 100 µL/well of goat anti-human IgG antibody diluted 1:1,000 horseradish peroxidase-conjugated antibodies (Caltag Laboratories, Burlingame, CA). Colorimetric development was obtained as follows: 100 µl/well tetra-methyl-benzidine incubated for 10 minutes at room temperature. The reaction was stopped with 100 µL/well of 1 M phosphoric stop solution, and absorbance was measured at 450 nanometers. Each sample was tested in triplicate with three controls per plate: 1) control blank: 2 wells without 4G2 or DENV to control for nonspecific induction of color for any of the reagents used in the assay; 2) negative control: 2 wells with 4G2 and no DENV to control for any nonspecific color induction of the coating antigen; and 3) positive control from human serum with a known concentration of anti-DENV IgG antibodies.

Clinical and Etiologic Definitions

A dengue case was defined as a patient with molecular detection of DENV RNA, of any serotype, by RT-qPCR, without consideration of anti-DENV IgM or IgG antibody status. Patients were classified according to their infection status in by the following PCR-outcome categories:

DENV-negative Patients without molecular detection of DENV RNA DENV-positive Patients with molecular detection of DENV RNA

DENV SSI DENV-positive patients with infection by 1 serotype DENV-1 DENV-positive patients with infection due to DENV-1 only DENV-2 DENV-positive patients with infection due to DENV-2 only DENV-3 DENV-positive patients with infection due to DENV-3 only DENV-4 DENV-positive patients with infection due to DENV-4 only DENV MSI DENV-positive patients with infection by >1 serotype Leptospira-positivePatients with molecular detection of gene LipL32

Clinical and laboratory data were collected using the information queried by the CDC Dengue Case Investigation Report (Appendix F). Due to the lack of population-standardized hematotologic indices for the Colombian adult or pediatric populations, we applied the clinical reference ranges published by the American Board of Internal Medicine for adults, and those used by Texas Children’s Hospital Department of Anesthesiology and Pediatrics (Appendix G) for pediatric patients.

Research Objectives

Hypothesis 1: The means of the identified hematologic indices will be different and indicative of greater clinical severity in DENV MSI versus DENV SSI, such that:

Hox: Group 5 = Group 4 Hoy: Group 5 = Group 4 Hoz: Group 5 = Group 4

Hax: Group 5 < Group 4 Hay: Group 5 < Group 4 Haz: Group 5 > Group 4 𝑥𝑥̅ 𝑥𝑥̅ 𝑦𝑦� 𝑦𝑦� 𝑧𝑧̅ 𝑧𝑧̅ Rationale𝑥𝑥 ̅ 𝑥𝑥̅ 𝑦𝑦� 𝑦𝑦� 𝑧𝑧̅ 𝑧𝑧̅ Hematologic indices are clinical predicators of severity. We identified the following endpoints to define clinical severity: 1) Leukocytes (x) = where is the mean leukocyte count; 2) Platelets (y) = is the mean platelet count; and 𝑥𝑥̅ 3) Hematocrit (z) = where is the mean hematocrit. 𝑦𝑦� The central importance of these 𝑧𝑧indices̅ in DENV pathogenesis and clinical presentation is supported by the following observations: 1) Leukopenia, defined by WHO as <5.0 x 109 leukocytes/L for adults,25 is a hallmark feature of the early phase (i.e., DPO 2-10) of dengue. It occurs in 76-100% of dengue cases and is associated with viral destruction or inhibition of mononuclear lineage cells. 2) Thrombocytopenia, defined by WHO as <100.0 x 109 platelets/L for pediatric and adult patients,25 is a major clinical manifestation of moderate-severe dengue occurring DPO 3.5. The estimated prevalence of thrombocytopenia is 26-50%.25 It is associated with capillary permeability secondary to endothelial dysfunction in the progression of dengue from the febrile to the critical phase. 3) Hemoconcentration, defined by WHO as a change in hematocrit >20% above baseline for adults.25 It is a major clinical manifestation of moderate-severe dengue and an indicator of dysfunction of the endothelial barrier and plasma leakage into the perivascular space.25

Hypothesis 2: One or more clinical characteristics or hematologic indices differentiating DENV- Leptospira co-infected patients from DENV-positive patients can be incorporated into a clinical algorithm for use in under-resourced clinical settings.

Ho: Due to the identical clinical presentation of dengue and leptospirosis in the acute phase (i.e., DPO <7), no clinical characteristic or hematologic index is able to differentiate DENV- Leptospira co-infection from DENV SSI and provide decisional support for clinicians without access to comprehensive diagnostic testing.

Ha: Despite the identical clinical presentation of dengue and leptospirosis in the acute phase (i.e., DPO <7), one or more clinical characteristic(s) or hematologic index(ices) is able to differentiate DENV-Leptospira co-infection from DENV SSI to provide decisional support for clinicians without access to comprehensive diagnostic testing.

Rationale Leptospirosis is a differential diagnosis for tropical AFI,26 and leptospirosis is frequently misdiagnosed as dengue.13 A case-control study in Bucaramanga, Colombia,27 demonstrated significant differences in leukocyte and platelet counts between patients with dengue and patients with leptospirosis, supporting the utility of these indices as part of a diagnostic algorithm. The central importance of investigating DENV-Leptospira co-infections is supported by the following: 1) Dengue and leptospirosis have overlapping geographic regions of endemnicity28; 2) Dengue and leptospirosis have identical acute clinical presentations27; 3) Laboratory diagnosis of leptospires is highly technical, lengthy, and not readily available 29; 4) Leptospirosis is a disease of high consequence but is treatable by cost-effective, readily-available antibiotics.9,12

Statistical Analyses

Frequency of Demographic and Clinical Characteristics The prevalence of demographic characteristics was expressed as numbers and percentages. Categorical variables (i.e., clinical characteristics) were expressed as numbers and percentages. Group comparisons were performed using X2 test or Fisher’s exact tests, as appropriate for the sample sizes under comparison.

Comparison of Means for Hematologic Indices Parametric One-way ANOVA was used to compare the means of continuous variables (i.e., hematologic indices). Between-group mean differences were compared using the Student’s t test and the Mann-Whitney U test (Wilcoxon rank sum test) where applicable.

Logistic Regression Logistic regression was used to model the relationship between DENV infection status. Crude and adjusted odds ratios (OR) were used to assess measures of association.

Post-Hoc Analyses Two types of post-hoc analyses were performed. First, restricting the analyzable cohort to only DENV-positive patients, we used a case-case approach30 with DENV-positive patients as the case- control and differentially-infected subgroups (i.e., SSIs and MSIs) as case-case groups. Due to both zero values and small samples sizes in the outcome subgroups, case-case analyses could not be performed or failed to approach statistical significance. A second approach was a principal component analysis aimed at detecting signals of association between variables, particularly the interrelated hematologic indices.

Statistical analyses were performed in SAS version 9.4 ® (Cary, NC). P values were based on 2-sided tests with statistical significance at p <0.05. Confidence intervals were calculated at the 95% level.

RESULTS

Participant Demographics

Between January 2013 and October 2014, 216 patients with an AFI were enrolled and underwent molecular testing for DENV and Leptospira spp. Los Patios (n=124, 57.4%) and Ocaña (n=50, 23.4%) were overrepresented in the enrolled cohort; 64.1% (n=66) of DENV-positive patients were from Los Patios (Table 1, Figure 8). Although recruitment was continuous, 50% of patients were enrolled between October and December of 2013, presenting for care a mean of 3.6 DPO (range 2.0- 8.0 days). Timing of presentation for care did not differ by infection status (DPO 3.6 for DENV- positive versus DPO 4.0 for DENV-negative, p=0.1508), nor did admission to hospital (16.8% for DENV-positive versus 27.2% for DENV-negative, p=0.2003). Twenty-eight (27.2%) patients were hospitalized; 18 (64.3%) were pediatric patients. There were no differences between SSI and MSI patients for timing of presentation for care or admission to hospital.

Female patients comprised 44.4% (n=96) of the enrolled cohort; there were no pregnancies. Co- morbid conditions—trisomy 21 and hepatitis B virus (HBV)—were recorded for two patients (0.9%). The mean age for the enrolled cohort was 20.0 years (range <1-92 years), compared to 19.8 years (range 1-87 years) for the DENV-positive cohort. Pediatric patients (<18 years) comprised 63.0% (n=136) of enrollees; 34.9% (n=36) were between the ages of 1-9 years (Table 1).

Etiology

One hundred and three (47.7%) patients were positive for DENV (Figure 8). SSIs (n=88) accounted for 85.4% of DENV-positive infections and MSIs (n=15) 14.6% of all DENV-positive infections. Three patients were tested positive for infection with a pathogenic Leptospira spp., providing a cohort infection prevalence of 1.4%. Anti-DENV IgM and IgG antibody data were available for 91.2% (n=103) of DENV-negative patients and 49.5% (n=51) of DENV-positive patients. However, the data did not form the basis of classification of infection status, nor was it used to infer associations between acute etiology and clinical data.

Single- and Multi-Serotype Infection DENV-2 was the predominate SSI (n=34, 38.6%), followed by DENV-3 (n=24, 27.3%), DENV-1 (n=21, 23.9%), and DENV-4 (n=9, 10.2%) (Figure 8). DENV-2/4 (n=5) accounted for 41.7% of MSIs, followed by DENV-2/3 (n=4, 33.3%), DENV-3/4 (n=2, 16.7%), and DENV-1/2 (n=1, 8.3%). Triple-serotype combinations were DENV-1/2/3 (n=1), DENV-1/3/4, (n=1), and DENV-2/3/4) (n=1).

Figure 9. Patients with Dengue and Leptospirosis, by Municipality of Residence Numbers inside colored indicate number of patients. Base map from the Colombian Ministry of Health and Social Protection.

Clinical Presentation

Headache (52.3%) and myalgia (49.1%) were the most frequently reported symptoms for all enrolled patients, similar to DENV-negative patients (headache 79.3%, myalgia 75.0%) (Table 2). DENV- positive patients reported headache (52.4%), myalgia (49.5%), and vomiting (27.2%). Abdominal pain was reported by 46.2% (n=6) of patients with DENV-1 SSI, and DENV-2 SSI patients reported retro-orbital pain (n=11, 52.6%). Signs of bleeding were observed in seven (6.8%) patients (Table 3).

Table 1. Seroprevalence Survey Descriptive Data for Dengue-Positive and Negative Patients

Table 2 . Comparative Frequencies of Presenting Symptoms, for all Enrollees and by Dengue Virus Infection Status All Participants DENV-positive DENV-negative

Clinical Feature P value* N (%) N (%) N (%)

Abdominal pain 33 (15.3) 17 (16.5) 15 (25.9) 0.1832

Arthralgia 35 (16.2) 14 (13.6) 17 (29.3) 0.3030

Diarrhea 7 (3.2) 5 (4.9) 2 (3.5) 0.3075

Epistaxis 4 (1.9) 2 (1.9) 2 (3.5) 0.5283

Gingival bleeding 1 (0.5) 1 (1.0) 1 (1.7) 1.0000

Headache 113 (52.3) 54 (52.4) 46 (79.3) 0.5493

Icterus 1 (0.5) 1 (1.0) 0 (0) †

Hospitalized 53 (24.5) 28 (27.2) 25 (22.1) 0.9800

Hypotension‡ 1 (0.5) 1 (1.0) 0 (0) †

Myalgia 106 (49.1) 51 (49.5) 15 (75.0) 0.3418

Oliguria 3 (1.4) 3 (1.4) 0 (0) †

Petechiae 6 (2.8) 1 (1.0) 3 (5.2) 0.4364

Rash 26 (12.0) 12 (11.7) 13 (22.4) 0.0783

Retro-orbital pain 58 (26.9) 24 (23.3) 27 (46.6) 0.1946

§ Tachycardia 1 (0.5) 1 (1.0) 0 (0) † Vomiting 46 (21.3) 28 (27.2) 14 (24.1) 0.7528

Abbreviations: DENV, dengue virus. * Fisher's exact X 2 p -value significant at < 0.05. † Not applicable due to zero values. ‡ Blood pressure <90/60 mm Hg. § Heart rate >100 beats per minute.

Table 3 . Comparative Frequencies of Presenting Symptoms, Dengue Virus SSIs and MSIs

DENV-1 only DENV-2 only DENV-3 only DENV-4 only MSI only

Clinical Feature N (%) N (%) N (%) N (%) N (%)

Abdominal pain 6 (46.2) 4 (21.1) 4 (23.5) 2 (25.0) 1 (6.7)

Arthralgia 2 (15.4) 5 (26.3) 4 (23.5) 2 (25.0) 1 (6.7)

Diarrhea 1 (7.7) 1 (10.5) 1 (5.9) 0 (0) 0 (0)

Epistaxis 0 (0) 3 (15.8) 0 (0) 0 (0) 0 (0)

Gingival bleeding 0 (0) 0 (0) 0 (0) 0 (0) 1 (6.7)

Headache 8 (61.4) 17 (89.5) 13 (76.5) 8 (100.0) 8 (53.3)

Icterus 0 (0) 0 (0) 1 (5.9) 0 (0) 0 (0)

Hospitalized 4 (30.8) 11 (57.9) 5 (29.4) 3 (37.5) 3 (20.0)

Hypotension* 0 (0) 1 (10.5) 0 (0) 0 (0) 0 (0)

Myalgia 9 (69.2) 14 (73.7) 15 (76.5) 8 (100.0) 7 (46.7)

Oliguria 1 (7.7) 1 (5.3) 1 (5.9) 0 (0) 0 (0)

Petechiae 0 (0) 1 (5.3) 2 (11.8) 0 (0) 0 (0)

Rash 2 (15.4) 3 (15.8) 2 (11.8) 1 (12.5) 4 (26.7)

Retro-orbital pain 4 (30.8) 10 (52.6) 6 (35.3) 2 (25.0) 2 (13.3)

Tachycardia † 0 (0) 0 (0) 1 (5.9) 0 (0) 0 (0) Vomiting 0 (0) 7 (36.8) 7 (41.2) 3 (37.5) 5 (33.3)

Abbreviations: DENV, dengue virus; MSI, multi-serotype infection; SSI, single-serotype infection. * Heart rate >100 beats per minute. † Blood pressure <90/60 mm Hg.

Table 4. Mean Hematologic Indices, by Dengue Virus Infection Status

DENV-negative DENV-positive DENV-1 DENV-2 DENV-3 DENV-4 DENV MSI

Index Mean ± SD Mean ± SD Mean ± SD Mean ± SD Mean ± SD Mean ± SD Mean ± SD P value (Range) (Range) (Range) (Range) (Range) (Range) (Range)

Erythrocytes 4.9 ± 0.5 5.2 ± 0.6 4.96 ± 0.6 5.3 ± 0.6 4.8 ± 0.3 5.4 ± 0.5 5.34* 0.7123 (109/L) (3.8-6.1) (4.1-6.5) (4.1-6.0) (4.4-6.5) (4.5-5.3) (4.8-5.9)

Hematocrit 39.6 ± 5.6 40.8 ± 4.7 40.4 ± 5.5 40.1 ± 9.0 40.1 ± 4.0 40.8 ± 3.6 40.8 ± 4.7 0.9616 (%) (5.0-52.9) (12.0-55.6) (32.0-55.6) (12.0-54.6) (33.0-48.8) (36.7-45.2) (33.0-50.8)

Hemoglobin 12.9 ± 1.7 13.1 ± 1.9 13.0 ± 2.0 15.0 ± 6.4 12.7 ± 1.5 13.1 ± 1.4 13.1 ± 1.9 0.0232 (g/dL) (8.7-17.9) (8.8-39.4) (10.0-18.1) (8.8-39.4) (10.0-15.6) (11.6-15.3) (10.2-16.3)

Leukocytes 4.4 ± 2.3 3.9 ± 2.0 3.7 ± 2.4 4.4 ± 2.1 3.8 ± 1.7 3.4 ± 1.1 3.9 ± 2.0 0.5382 (109/L) (1.0-12.2) (1.3-11.3) (1.5-11.3) (1.8-10.5) (1.4-7.4) (2.3-5.2) (1.3-9.6)

Lymphocytes 42.4 ± 24.6 44.0 ± 17.6 42.9 ± 22.0 39.8 ± 22.2 43.3 ± 22.9 36.5 ± 11.8 36.5 ± 11.8 0.9546 (%) (2.0-90.0) (2.5-89.0) (7.2-84.0) (5.7-89.0) (4.9-86.0) (15.8-56.0) (2.5-79.0)

Neutrophils 53.3 ± 24.2 53.3 ± 17.2 52.8 ± 20.5 54.8 ± 23.1 54.6 ± 23.0 60.4 ± 11.2 53.3 ± 17.2 0.9649 (%) (10.0-94.5) (10.0-94.1) (14.0-89.6) (10.0-91.0) (14.0-92.1) (44.0-76.9) (21.0-94.1)

Platelets 99.9 ± 38.6 102.3 ± 43.5 101.2 ± 38.3 81.8 ± 47.3 110.1 ± 40.8 103.1 ± 36.5 102.3 ± 43.6 0.1559 (109/L) (15.0-191.0) (9.0-184.0) (29.0-184.0) (9.0-145.0) (23.0-161.0) (32.0-135.0) (16.0-174.0)

Abbreviations: DENV, dengue virus; L, liter; g/dL, grams per deciliter; L, liter; MSI, multi-serotype infection' SD, standard deviation. * N=1. Significant at p <0.05.

Hematology

Hemoglobin and Hematocrit Despite the correlation between hemoglobin and hematocrit, and their dual-dependence on whole blood and plasma volume,31 this biological correlation did not hold in our analyses. DENV-2 had the highest mean hemoglobin (15.0 + 16.4 g/dL), consistent with hemoconcentration, but there were no significant between-group differences (Table 5).

Table 5. Between-Group Differences in Mean Hemoglobin

Group 1 Group 2 1- 2 95% CI* (g/dL) 𝑥𝑥̅ 𝑥𝑥̅ DENV-2 DENV-negative 2.1 0.90-3.22

DENV-2 DENV-1 2 0.33-3.63

DENV-2 DENV-3 2.2 0.61-3.86

DENV-2 DENV-4 1.81 -0.42-4.03

DENV-2 DENV MSI 1.9 0.09-3.70

Abbreviations: DENV, dengue virus; g/dL, grams per deciliter.

1 - 2 Difference between mean values of group 1 and group 2. *t-test significant at p <0.05. 𝑥𝑥̅ 𝑥𝑥̅

The mean hematocrit for the enrolled cohort (40.8%) was statistically equivalent across all groups (p=0.9616, data not shown), while the mean hemoglobin was significantly different across all groups (p=0.0232) (Table 4). In the principle component analysis, no significant associations emerged, including between hemoglobin and hematocrit. The extent of missing data (Appendix I), particularly for DENV-negative patients, likely contributed to this counterintuitive finding.

Leukocytes The mean leukocyte count for all enrollees was 3.9 x 109 cells/L, which falls just below the lower limit of the adult clinical reference range of 4.0 x 109 leukocytes/L (Appendix G). The DENV-2 SSI group 9 did not have leukopenia ( leukocyte count = 4.4 ± 2.1 x 10 cells/L) and was the only infection group with a mean leukocyte count exceeding the cohort mean (4.4 ± 2.3 x 109 cells/L), although the difference was not statistically significant𝑥𝑥̅ (p=0.5382) (Table 4). In regression analyses (Table 6), only DENV-1 was marginally associated with leukopenia (OR 2.85; 95% CI 1.05-7.74), which, when adjusted for age (aOR 2.82; 95% CI 1.02-7.79) and DPO (aOR 2.84; 95% CI 1.05-7.72) weakened but remained significant.

Platelets All enrolled patients met the clinical criteria for thrombocytopenia (<150.0 x 109 platelets/L) with a cohort mean of 102.3 + 43.5 x 109 platelets/L. We observed no overall association between platelet count and DENV infection status (p=0.1559). DENV-2 SSI patients had the lowest mean platelet count (81.8 + 47.3 x 109/L), and DENV-3, the highest (110.1 + 40.8 x 109 /L). However, there were between-group differences for DENV-2 SSI patients. Compared to DENV-negative patients, DENV- 2 SSI patients had a significantly lower platelet count ( =28.3 platelets, CI 6.70-49.91), as did DENV- 3 SSI patients when compared to DENV-2 SSI patients ( =18.1 platelets, CI 2.42-33.75) (Tables 4 and 7). Neither SSI nor MSI predicted severity of thrombocytopenia𝑥𝑥̅ . 𝑥𝑥̅ Table 6. Predictive Models of Leukopenia and Dengue Virus Serotype 1

Model Parameter OR (95% CI) P value

DENV infection status x leukocytes DENV-1 2.85 (1.05-7.74) 0.0394

DENV infection status x leukocytes DENV-1 2.82 (1.03-7.69) 0.0432 Adjusted for Age

DENV infection status x leukocytes DENV-1 2.84 (1.05-7.72) 0.0401 Adjusted for DPO

DENV infection status x leukocytes DENV-1 2.76 (1.02-7.51) 0.4640 Adjusted for Sex

Abbreviations: DENV, dengue virus; L, liter. X2 p -value significant at <0.05.

Table 7. Between-Group Differences in Mean Platelet Counts

1- 2 Group 1 Group 2 N 95% CI* (109/L) 𝑥𝑥̅ 𝑥𝑥̅ DENV-2 DENV-negative 68 28.3 6.70-49.91

DENV-2 DENV-3 147 18.1 2.42-33.75

Abbreviations: DENV, dengue virus; g/dL, grams per deciliter. 1 - 2 Difference between mean values of group 1 and group 2. t p *𝑥𝑥̅ -test𝑥𝑥̅ significant at <0.05. 38

Multi-Serotype Infection and Clinical Severity

Adult Patients The three adult patients were infected with a serotype combination that included DENV-2, and each patient had thrombocytopenia and monocytosis (Table 9). Clinical symptoms at presentation were documented for only one patient, a 62-year-old female (DENV-2/3) from Los Patios who presented for care on DPO 4 complaining of headache and myalgia.

Pediatric Patients Twelve of 15 (80.0%) MSIs were in patients <15 years of age (median 8.5 years, range 1-15 years) (Tables 10-12). Males accounted for 75.0% (n=9) of pediatric MSIs. Ten (83.3%) resided in Los Patios, followed by Teorama (n=3, 25.3%) and Ocaña (n=2, 16.7%) (Figure 9). They presented for care a median of DPO 3 (range 3-4 days). DENV-2/4 was the most frequent serotype combination (n=4, 33.3%) (Table 11). Three patients <16 years of age were simultaneously infected with three serotypes (Table 12). Headache (n=6, 50.0%) and myalgia (n=6, 50.0%) were recorded for half of the pediatric MSIs (Tables 10-12). All 12 had thrombocytopenia; nine (75%) were classified as mild, two (16.7%) moderate, and one (8.3%), a three-year-old male, with borderline severe thrombocytopenia. Gingival bleeding was documented in a two-year old male with a platelet count of 75.0 x 109 cells/L. Leukopenia (n=10, 83.3%) was the second most frequently observed condition, followed by anemia (n=4, 33.3%).

Leptospira-Positive Patients

Table 13 provides demographic, clinical, and hematologic data for Leptospira-positive patients. Each of the three patients was a resident of Los Patios, and two patients were co-infected with DENV. Arthralgia, myalgia, thrombocytopenia were common to the three patients; the DENV-negative and DENV-2 co-infected patients had moderately severe leukopenia. Based on the hematologic indices, there are indicators of additional etiologies other than those for which we conducted molecular testing. The DENV-negative patient, who presented with eosinophilia, could have a parasitic infection or other systemic inflammatory process. The patient co-infected DENV-2, who was the only patient admitted to hospital, had a clinical presentation consistent with a hematologic malignancy. Finally, the patient with DENV-3 and HBV had a clinical picture consistent with HIV infection, liver cirrhosis, and a neutrophil-to-lymphocyte ratio associated with a poor prognosis in patients with HBV.32

DISCUSSION

Little is known about the processes and consequences of DENV MSI, including their frequency and impact on regional transmission dynamics. In Norte de Santander, which includes the Colombian- Venezuelan border, neither the prevalence of MSIs nor the prevalence of leptospirosis have been determined. Employing hospital-based surveillance methodology, we tested the sera of 216 AFI

patients presenting for care in at two referral centers. We confirmed 103 cases of dengue, of which 14.6% were MSIs, and three cases of leptospirosis, of which two were in patients with dengue. To our knowledge, this is the first study to report DENV MSIs and dengue-leptospirosis co-infection in this region of Colombia.

DENV-2 and -3 were detected with greater frequency than the other serotypes during our study period from 2013-2014. A study conducted one year later in Norte de Santander, from 2015-2016, identified DENV-2 and -1 as the dominate serotypes in circulation for that period.33 As an aside, this study, which used hospital-based serosampling, also detected DENV-CHIKV and DENV-ZIKV co- infections in 40% of serum samples. Three patients were acutely infected with all three arboviruses.

Indicators of Clinical Severity

As a clinical syndrome, AFIs present both a diagnostic and treatment challenge for clinicians, particularly in developing countries. Dengue has long been identified as a clinical syndrome identified initially presenting with fever, myalgia, and headache.29 The difficulty, therefore, in diagnosing dengue has been the non-specific presentation of symptoms that mirror other AFIs. Thrombocytopenia and leukopenia were prevalent in this study population, including among the DENV-negative patients. Further, headache and myalgia, the most frequently reported symptoms by DENV-positive patients, did not differ by individual serotype of between SSIs and MSIs. Our findings underscore the challenge of dengue diagnosis: clinical presentation alone is insufficient.

Overall, no patterns emerged within the MSI cohort to indicate differences in either magnitude or severity. Clinically, the pediatric population is distinct from the adult population, and we were underpowered to detect differences between adult and pediatric patients. Due to small sample sizes, which limited statistical power, we grouped patients by infection status, ignoring sex-linked physiologic differences that contribute to clinical outcomes for infection diseases. In case-level examination, however, pediatric thrombocytopenia did not appear to differ by SSI or MSI, nor did the other hematologic indices. A larger study, powered to detect differences by age-sex groups, will be required to further explore the association of clinical severity and MSI.

Leptospirosis

The National Public Health Surveillance Center for Colombia has one documented case of leptospirosis in Norte de Santander, which was in 2007.34 Ours is the second report of leptospirosis in Norte de Santander, and the first to identify co-infections with DENV. We estimate the prevalence of leptospirosis to be 1.4%, which is almost certainly an underestimation of the true burden of disease in the region.

Arthralgia, myalgia, and mild thrombocytopenia were the common characteristics of Leptospira- positive patients. Leukopenia was observed in the one patient with DENV co-infection and in the DENV-negative patient. Leukopenia has been suggested as a potential clinical discriminator between

40 dengue and leptospirosis,12,27 although our findings do not reflect this relationship. Conversely, the patient with dengue, leptospirosis, and HBV did not have leukopenia, but he did have neutrophilia. In a comprehensive case report of fatal DENV-Leptospira co-infection in Puerto Rico, thrombocytopenia, leukocytosis, with neutrophilia were each observed.12 Neutrophilia is a common finding in patients with HBV and, together with the neutrophil-to-lymphocyte ratio, is a prognosticator of HBV-associated mortality.32 This patient highlights the difficulty for clinicians in interpreting clinical data and determining differential diagnosis. This unexpected finding further emphasizes the ability of case-level data to examine how co-infection can alter the clinical presentation of disease.

Our results are subject to a number of limitations and should be considered within the context of logistical limitations inherent in global health partnerships. The main challenge to data analysis was the amount of missing data (Appendix I). Although initially powered for detecting differences among the study groups, enrollment fell short of the planned samples sizes, for reasons not related to enrollment or study-site participation. Our use of hospital-based convenience sampling in Norte de Santander limits the generalizability of the results to the general population of Colombia, as patients seeking treatment may have increased severity, including underlying comorbidities, greater that the experience of the general population. Finally, our study was not designed for a pediatric population, nor did we use pediatric specialists to evaluate these patients. For example, the finding of retro-orbital pain in a pre-verbal pediatric patient should be considered with in this context.

A major strength of this study was the examination of hematologic values to evaluate disease severity. This enabled identification of clinical indicators that would be of utility at presentation for care, and thereby mediate a shorter interval between onset of illness and inhiation of supportive or antibiotic therapies. Further, our use of PCR platforms for diagnosing dengue and leptospirosis avoided the ambiguity of antibody response and increased the specificity of inferences about pathogen-specific presentation. However, we did not perform additional testing for other etiologies, and therefore we cannot rule out confounding by other chronic and infectious conditions.

Implications for Clinical and Public Health Practice

Our objectives for this study were twofold: 1) to establish the first prevalence estimates of DENV MSIs and Leptospira co-infections in Norte de Santander; 2) to design an algorithm to discriminate between the clinical presentations of dengue SSI and MSI, and dengue and dengue-leptospirosis. Our rationale for these objectives is the increasing frequency of co-infections and the need to explore how these infections alter disease severity. Although dengue has no pharmaceutical therapy, leptospirosis is treatable with readily-available antibiotics. Because of the small numbers of both types of infection, we identified case-level data as a source of granularity not achieved by the use of aggregate data.

We provided estimates, with limitations, of the prevalence of DENV MSI and DENV-Leptospira co- infections, but we were unable to conclude that a difference in clinical severity between the acute presentation of dengue SSI is different than that of dengue MSI. While we detected serotype-specific

41 clinical features that invariably present across the spectrum of dengue, including leukopenia and thrombocytopenia, we failed to identify clinical features or hematologic measures of clinical severity in the MSI patient, although assessment of this relationship using a larger cohort of patients is warranted. The clinical data presented herein will be of utility for background information for pooled analyses for future studies of infection severity.

Under-diagnosis and under-reporting of leptospirosis may be due to a lack of clinician awareness of the prevalence of disease and environmental reservoirs, and consideration should be given to public campaigns and medical education throughout endemic regions. In Colombia, diagnosis of leptospirosis requires submission of acute and convalescent sera to private or referent laboratories for testing, a cost-prohibitive option for the patient and one too lengthy for delaying treatment. Increasing the availability of rapid diagnostics will be essential for defining the prevalence of leptospirosis, decreasing its incidence, and preventing the severe sequelae associated with untreated disease.

CONCLUSION

Multi-pathogen infections will continue to occur as emerging tropical viruses move into population centers where other diseases of poverty and urban density constitute an already high burden of disease. The broadening application of molecular technologies for clinical diagnosis, including the use of next- generation sequencing for describing the intra-host pathogen landscape, will detect these infections with increased frequency, requiring clinical interpretation for the treating clinician. In Norte de Santander, Colombia, we estimated the prevalence of multi-serotype dengue disease to be 14.6% and the prevalence of leptospirosis to be 1.4%. We identified co-infections of DENV with pathogenic Leptospira spp. and HBV. While we were unable to identify clinical or laboratory predictors of severity associated these co-infections, the implication of multi-pathogen infection on disease pathogenesis, clinical manifestation of disease, and pathogen-pathogen interaction should be the subject of targeted surveillance and investigation.

Table 8. Association of Thrombocytopenia with Serotype-Specific Infection and Multi-Serotype Infection

Table 9. Adult Multi -Serotype Infections

Clinical Presentation Hematology Sex DENV IgM Age Eryth Leu Hb Hct Plt Neut Lymph Mono Baso Eos Serotype (Ct) IgG DPO Symptoms Hospitalized City (x109/L) (x109/L) (g/dL) (%) (x109/L) (%) (%) (%) (%) (%)

Female 2 (36.68) 37 years IgG+ † † † 4.8 9.6 14.8 41.5 55.0↓ 32.8 55.1 5.8↑ 0.9 5.4↑ 4 (17.84) San Pablo

Male 2 (36.68) 37 years ND † † † 5.9 4.6 16.3 50.8 16.0↓ 55.3 33.0 9.7↑ 0.7 1.3 3 (17.84) San Pablo

Female 2 (36.29) Headache 62 years † 4 No † 3.8↓ 11.6↓ 37.0↓ 97.0↓ 62.0 36.0 † † 2.0 3 (37.86) Myalgia Los Patios

Abbreviations: Admit, admission to hospital; Baso, basophils; Ct, cycle Above (↑) or below (↓) laboratory reference for age and sex. See Appendix 8 for adult and pediatric threshold; DENV, dengue virus; DPO, days post-illness onset; Eos, eosinophils; reference ranges. Eryth, erythrocytes; Hb, hemoglobin; Hct, hematocrit; IgG, anti-DENV IgG † Missing value antibody; IgM, anti-DENV IgM antibody; Leu, leukocytes; Lymph, lymphocytes; Mono, monocytes; ND, none detected; Neut, neutrophils; Plt, platelets; and Yr, years.

Table 10. Pediatric Dengue Virus Multi-Serotype Infections

Clinical Presentation Hematology Sex DENV IgM Age Eryth Leu Hb Hct Plt Neut Lymph Eos Serotype (C IgG DPO Symptoms Hospitalized City t) (x109/L) (x109/L) (g/dL) (%) (x109/L) (%) (%) (%)

Headache Female 1 (29.00) Myalgia 8 years IgG+ 3 No † 2.3↓ 14.3 45 99.0↓ 60.0 40.0 † 2 (29.80) Rash Los Patios Retro-orbital pain

Male Gingival bleeding 2 (21.68) IgM+ 2 years 3 Headache No † 5.0 12.5 38.0 75.0↓ 42.0 58.0 † 3 (30.23) IgG+ Los Patios Vomiting

Male 2 (36.29) 13 years IgG+ 3 Myalgia No † 3.6↓ 13.5 43.0 149.0↓ 50.0 50.0 † 3 (37.86) Los Patios

Male 3 (33.40) 3 years ND † † † 5.3 4.7↓ 13.4 42.5 58.0↓ 64.7 30.8 0.2 4 (13.76) San Pablo

Male 3 (35.47) 10 years IgG+ 4 † Yes † 2.7↓ 12.9 41.0 110.0↓ 57.0 43.0 † 4 (31.84) Los Patios

Abbreviations: Admit, admission to hospital; Baso, basophils; Ct, cycle threshold; Above (↑) or below (↓) laboratory reference for age and sex. See Appendix # for DENV, dengue virus; DPO, days post-illness onset; Eos, eosinophils; Eryth, adult and pediatric reference ranges. erythrocytes; Hb, hemoglobin; Hct, hematocrit; IgG, anti-DENV IgG antibody; † Missing value IgM, anti-DENV IgM antibody; Leu, leukocytes; Lymph, lymphocytes; Mono, monocytes; ND, none detected; Neut, neutrophils; Plt, platelets; and Yr, years. 45

Table 11. Pediatric Multi-Serotype Infections with Dengue Virus Serotypes 2 and 4

Clinical Presentation Hematology Sex DENV IgM Age Eryth Leu Hb Hct Plt Neut Lymph Eos Serotype (C IgG DPO Symptoms Hospitalized City t) (x109/L) (x109/L) (g/dL) (%) (x109/L) (%) (%) (%)

Male Headache 2 (30.24) 1 year † 3 Rash No † 6.1 10.2↓ 33.0 132.0↓ 27.0 72.0 1.0 4 (29.80) Los Patios Vomiting

Female 2 (24.82) Myalgia 4 years † 3 No † 3.8↓ 10.6↓ 36.0 174.0↓ 21.0 79.0 † 4 (18.41) Rash Los Patios

Female Headache 2 (27.10) 8 years IgG+ 3 Myalgia No † 3.0↓ 11.4↓ 37.0 131.0↓ 67.0 33.0 † 4 (22.65) Los Patios Vomiting

Headache Male 2 (27.29) Myalgia 9 years IgG+ 3 Yes † 3.0↓ 11.8 38.0 161.0↓ 55.0 45.0 † 4 (21.67) Retro-orbital Los Patios pain

Abbreviations: Admit, admission to hospital; Baso, basophils; Ct, cycle threshold; DENV, Above (↑) or below (↓) laboratory reference for age and sex. See Appendix # for adult and dengue virus; DPO, days post-illness onset; Eos, eosinophils; Eryth, erythrocytes; Hb, pediatric reference ranges. hemoglobin; Hct, hematocrit; IgG, anti-dengue virus IgG antibody; IgM, anti-dengue virus † Missing value IgM antibody; Leu, leukocytes; Lymph, lymphocytes; Mono, monocytes; ND, none detected; Neut, neutrophils; Plt, platelets; and Yr, years.

Table 12. Dengue Virus Triple -Serotype Infection

Clinical Presentation Hematology Sex DENV IgM Age Eryth Leu Hb Hct Plt Neut Lymph Mono Baso Eos Serotype (C IgG DPO Symptoms Hospitalized City t) (x109/L) (x109/L) (g/dL) (%) (x109/L) (%) (%) (%) (%) (%)

Male 2 (32.33) 9 years 3 (30.66) IgG+ 3 None No † 1.8↓ 11.4↓ 37.0 118.0↓ 55.0 42.0 † † 3.0 Los Patios 4 (15.99)

Male 1 (34.70) 12 years 3 (10.73) ND † † † 5.1↑ 2.5↓ 15.2 45.0 104.0↓ 58.2 37.1 2.8 0.4 1.5 Ocaña 4 (13.74)

Male 1 (27.79) Headache 15 years 2 (24.04) † 3 Yes † 1.3↓ 13.0 40.0 110.0↓ 52.0 48.0 † † † Myalgia Los Patios 3 (19.12)

Abbreviations: Admit, admission to hospital; Baso, basophils; Ct, cycle Above (↑) or below (↓) laboratory reference for age and sex. See Appendix # for adult and pediatric threshold; DENV, dengue virus; DPO, days post-illness onset; Eos, reference ranges. eosinophils; Eryth, erythrocytes; Hb, hemoglobin; Hct, hematocrit; IgG, † Missing value anti-dengue virus IgG antibody; IgM, anti-dengue virus IgM antibody; Leu, leukocytes; Lymph, lymphocytes; Mono, monocytes; ND, none detected; Neut, neutrophils; Plt, platelets; and Yr, years.

Table 13. Dengue Virus-Leptospira spp. Co-Infections

Clinical Features Hematologic Indices Sex, Age DENV DENV Leu Hb Hct Plt Neut Lymph Eos City Serotype Antibody DPO Hospitalized Symptoms (x109/L) (g/dL) (%) (x109/L) (%) (%) (%)

F, 16 yr Arthralgia Negative IgG+ 3 No 2.6↓ 13.8 43.9 106.0↓ 63.0 29.0↓ 8.0↑ Los Patios Myalgia

Arthralgia M, 21 yr 2 IgG+ 6 Yes Myalgia 2.8↓ 13.4↓ 43.0 112.0↓ 22.0↓ 76.0↑ 2.0 Los Patios Vomiting

Arthralgia Diarrhea M, 33 yr Heachache 3 IgG+ 3 No 4.4 12.6↓ 38.8↓ 117.0↓ 85.0↑ 15.0 † Los Patios Hepatitis B virus Myalgia Retro-orbital pain

Abbreviations: DENV, dengue virus; DPO, days post-illness onset; Above (↑) or below (↓) laboratory reference range for age and sex (Appendix 8). Eos, eosinophils; Hb, hemoglobin; Hct, hematocrit; IgG, anti- † Missing value DENV IgG antibody; Leu, leukocytes; Lymph, lymphocytes; Neut, neutrophils; Plt, platelets; and Yr, years.

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29. Sharp TM, Tomashek KM, Read JS, et al. A new look at an old disease: recent insights into the global epidemiology of dengue. Curr Epidemiol Rep. 2017;4(1):11–21.

30. Pogreba–Brown K, Ernst K, Harris RB. Case–case methods for studying enteric diseases: a review and approach for standardization. OA Epidemiol. 2014;2(1):7.

31. Billett HH. Hemoglobin and hematocrit. In: Walker HK, Hall WD, Hurst JW, eds. Clinical Methods: The History, Physical, and Laboratory Examinations. 3rd ed. Boston, MA: Butterworths; 1990.

32. Gong J, Liang YL, Zhou W, et al. Prognostic value of neutrophil–to–lymphocyte ratio associated with prognosis in HBV–infected patients. J Med Virol. 2018;90(4):730–735.

33. Carrillo–Hernández MY, Ruiz–Saenz J, Villamizar LJ, et al. Co–circulation and simultaneous coinfection of dengue, chikungunya, and Zika viruses in patients with febrile syndrome at the Colombian–Venezuelan border. BMC Infect Dis. 2018;18(1):61.

34. Bello S, Rodríguez M, Paredes A, et al., Comportamiento de la vigilancia epidemiológica de la leptospirosis humana en Colombia, 2007–2011. Biomédica. 2013;33(suppl 1):153–60.

CHAPTER III INDIVIDUAL BEHAVIORS, SOCIAL PRESSURES, AND FAILURES OF EPIDEMIC CONTROL MEASURES THAT CONTRIBUTED TO TRANSMISSION OF ZAIRE EBOLAVIRUS AMONG GEOGRAPHICALLY-DISPERSED FAMILY MEMBERS, LIBERIA

A single introduction of EBOV can set off a chain of rapid transmission that accelerates through interconnected populations through unregulated border crossings, and all undetected due to a lack of public health infrastructure. In December 2013, EBOV emerged for the first time in West Africa. The index case, a two-year-old child in a rural village near the international borders of Guinea, Sierra Leone, and Liberia, was that single introduction (Figure 10).1 In March 2014, the first cases of EVD were detected in Lofa County, Liberia, with onward transmission to rural villages and the capital city, Monrovia. On 23 March 2014, MSF notified WHO of the outbreak, which had overwhelmed humanitarian efforts to isolate and care for cases, and sent affected communities into social turmoil.2 Not until 139 days later did the WHO respond, on 8 August 2014, with an issuance of a ‘public health emergency of international concern’.3 The most extensive and protracted outbreak of EBOV unfolded, infecting at least 28,616 people.4 Community resistance,5 the collapse of an already inadequate public health system, and a grossly inadequate supply of isolation beds contributed to unabated transmission through urban centers and rural areas,6 in a location and on a magnitude previously inconceivable.

We reconstructed a chain of transmission of EBOV that originated in a single source and spread to five, geographically-dispersed family members. Through the use of case-level data, we describe the physical contacts with the source patient, the circumstances of those contacts, and rarely-documented single-event contact between and infected source and secondary cases. We provide estimates of epidemiologic parameters of transmission to inform outbreak dynamics, providing granular knowledge applicable to current and future outbreaks of ebolaviruses. Coupled with case-level data, we explain how an inadequate capacity of isolation beds early in the outbreak, at the most crucial intervals, perpetuated transmission and contributed to community mistrust and resistance, violence, and refusals to accept control measures intended to interrupt outbreak progression. Finally, we identify underrecognized modes of transmission, which may account for EVD in persons with no identifiable risk factors, that should be the subject of community education efforts in future outbreaks of EVD.

METHODS

Ethical Considerations

This study was conducted during the international Ebola emergency response, in which the investigators participated in outbreak response efforts, direct patient care, clinical diagnostics, and infection prevention and control. The cases described herein were known to the investigators as part of a survivor cohort for which the authors assisted with community reintegration efforts and clinical

Figure 10. Map of Guinea, Liberia, and Sierra Leone International Borders Yellow arrowhead indicates the home village of the West Africa epidemic index case. Lofa County, where the first cases appeared in Liberia, is encircled in orange. Red arrows indicate spatial transmission into and through Liberia to the capital city, Monrovia. Bomi County is indicated by the blue circle and within the inset. The yellow circles identify the Klay checkpoint, where the source patient died at the roadside, and Tubmanburg, the location of the isolation and treatment centers, respectively. Map reproduced from the University of Texas Perry-Castañeda Library Map Collection. https://legacy.lib.utexas.edu/maps/liberia.html

evaluations of post-EVD sequelae. Human subjects approval was granted by the LSU IRB (Appendix J), and data collection conformed to all relevant General Principles of the Declaration of Helsinki.7 Cases and family members were informed of the purpose of the study, that their privacy would be protected, and that the findings would be shared within the international community to aid in understanding the events and behaviors that contribute to the transmission of EBOV. Written informed consent was obtained and witnessed by a third party. Cases were interviewed in their homes, alone or in the presence of family and community members, as per choice.

Constructing the Chain of Transmission

Detailed information was collected by standardized questionnaire; open interviews with cases, family and community members; and by interviews with staff and review of medical records at the district health clinic (DHC). We investigated, and cross-checked among varied data sources, exposure to the source patient, history of local and regional travel, contacts and types of physical interaction, dates of symptom onset, progression of clinical illness, and treatments. To create the fullest possible resolution of the chain of transmission, these data were supplemented by information collected from anecdotal reports, medications, and receipts obtained during health-seeking events by the cases. An exposure was defined as any physical contact with the source patient during the course of her illness, including contact with her body fluids and soiled linens.

Clinical Descriptors and Definitions of Phases of Ebola Virus Disease

We describe the evolution of EVD by exposure, onset of clinical symptoms, evolution of clinical disease, and outcome. We categorized EVD phase as dry and wet, according to the guidelines and 43- year history of use by Médecins Sans Frontières,8 the global humanitarian experts on viral hemorrhagic fever response and clinical management. Dry-phase symptoms include fever, myalgia, arthralgia, weakness, malaise, headache, and conjunctivitis. Wet-phase symptoms, including vomiting, diarrhea, abdominal pain, hemorrhage, profuse diaphoresis, subconjunctival hemorrhage, and mucosal bleeding.

Definition of Key Epidemiologic Parameters

To describe the transmission dynamics of EBOV in our cohort, we calculated the key intervals that correspond to transmission risk associated with the presence of virus in body fluids and tissues,9-11 and describe the clinical progression and outcomes of the six cases. The incubation period was calculated as the number of days between exposure to the source patient and symptom onset. For multi-day exposures, we used the first date of contact with the source patient during the wet or infectious phase of her disease. This was an intentional restriction to reflect the biological risk (i.e., probability) of virus transmission when virus is contained within the vascular system versus when virus is present in the tissues and extravascular fluids.10-11 The clinical onset serial interval (COSI) is the time between contact with the source patient in her wet phase and onset of dry-phase symptoms in a secondary case. The infectious period was determined by the date of wet-symptom onset to clinical outcome (i.e.,

recovery or death). Under epidemic conditions, recovery is necessarily defined as the date of discharge from the ETU, as real-time testing for the resolution of viremia was not, and could not, be performed in West Africa. Onset to clinical assessment for an epidemic of EVD is intended to define the number of days, and therefore approximate risk of ongoing transmission, that a symptomatic person spends in the community before recognition by a health care provider.11 We selected the onset of wet symptoms to more precisely describe the risk period of transmission using the same rationale for determination of the incubation period. Hospitalization to recovery/death was determined as the number of days between admission to the isolation unit and clinical outcome. Finally, the CFR was determined by dividing the number of deaths by six, corresponding to the number of cases in our chain of transmission.

Data Analysis

Due to our sample size of six persons, we calculated the median value where appropriate. The median for even-numbered data points was calculated by ranking the values in ascending order, averaging the two middle values, and dividing the sum by 2. For odd-numbered data points, the values were ranked in ascending order, with selection of the middle value.

RESULTS

Evolution of the West Africa Outbreak in Liberia

The first case of EVD in Liberia was reported in March 2014 in Lofa County, where five of six districts reported transmission.12 The virus reached the capital city of Monrovia in Montserrado County, seeding intense transmission and multiple focal outbreaks in rural and remote areas within and around Monrovia.13,14 By August 2014, Monrovia reported more than 200 incident cases of confirmed EVD per week,15 and Liberia recorded 2,080 suspected, probable, and confirmed cases for the month of September.16 By November, incident cases were concentrated in Monrovia and in smaller clusters in rural locations. In Bomi County, the markets and health centers were closed, and clinical staff was assigned to operate surveillance checkpoints. At the time of this transmission chain, there were no known sick persons in Bomi County. Only one source of exposure was reported by each of the surviving cases and by the family proxies for the decedent cases. Our investigation revealed the following sequence of events.

The Source Patient (Index Case) On 27 September 2014 (Figure 12), the source patient, a 42-year-old female trader originally from Lofa County, traveled 15 km by taxi from her village north of Monrovia to the Red Light District in Monrovia to attend the weekly market of traders traveling from Lofa County to sell their highly-prized palm oil. Four days later, on 1 October, she complained of feeling weak while selling goods in a nearby village. On 3 October, she was examined by the district health nurse (DHN), who recorded fever, weakness, and myalgia in the clinical dossier. She reported travel to Monrovia the week prior, but

denied contact with sick persons or attending a burial. The DHN suspected EVD because of her connection to Lofa County. No isolation beds were available in Monrovia or in Bomi County, and there were no options for testing. She was prescribed paracetamol and artesunate-amodiaquine in case of malaria. She was instructed to remain inside her home and contact the DHN in three days if her symptoms had not improved.

On 6 October, the source patient grew increasingly weak and developed a persistent, severe headache. She walked one km to visit the local roadside druggist in a neighboring village (Figure 11), where she purchased additional paracetamol and drugs indicated for malaria. Her symptoms continued to worsen, and her children left her house to stay with the grandmother, approximately 10 feet from the home of the index case. On 8 October, she began to have uncontrolled vomiting and diarrhea and was unable to walk. Her husband remained the sole care provider, and he did not allow anyone to enter the home. Family placed food and water on the porch, but there was no physical contact between the source patient and husband with outside persons.

On 13 October, the source-patient’s mother arrived to assist in her care. On October 15, the husband and mother took the source patient by taxi to a private medical clinic at Point Four Junction in the West Point area of Monrovia. The husband slept at a family home in Monrovia where there were no sick persons, and the mother slept on the clinic floor next to the source patient. The index case was administered three bags of IV fluids, doxycycline 200 mg daily, cimetidine 800 mg daily, and iron 400 mg daily for her diagnoses of typhoid fever, malaria, and a bleeding ulcer. The husband and mother reported that the clinic staff told them that the source patient tested negative for EBOV.

On 17 October, the source patient, husband, and mother traveled by taxi approximately 20 km from Monrovia to the DHC. Learning of her arrival, the DHN and district health officer (DHO) brought an ambulance to transport her to the isolation center in Tubmanburg. The family explained that the patient has tested negative for EBOV at the Monrovia clinic, and they ardently refused the ambulance because they ‘make people disappear.’ The index case remained at the mother’s home. Believing the 56

source patient to be EBOV-negative, her niece provided bedside care by wiping saliva and vomitus from her face, and her sister cleaned buckets of vomitus and hand-washed soiled linens in the creek. The niece reported washing her face with a cup of water used by the source patient and receiving paper money from her to purchase soap.

On 18 October, a brother-in-law arrived to assist the source patient and husband to travel by taxi to the isolation center in Tubmanburg. The brother-in-law assisted the index case into the back of the taxi, next to the husband, and then seated himself in the front passenger’s seat. The DHN spotted the source patient in a taxi and stopped the car. He noted no vomiting or diarrhea by the source patient, but noted she was profusely diaphoretic and had bright-red eyes that confirmed she was in the hemorrhagic phase. They again refused transport by ambulance and continued by taxi to the CCC in Tubmanburg. At Klay checkpoint five km from Tubmanburg, the source patient was denied passage and instructed to wait on the roadside for an ambulance. The brother-in-law assisted the source patient out of the taxi and hired a motorbike to return to his home in Tubmanburg. He did not wear a helmet, and no information is available about the motorbike operator. The source patient and husband waited separately from the crowd of people gathered at the Klay checkpoint. The source patient experienced intractable vomiting and died four hours later. The husband carried her body behind the checkpoint station and waited one hour for the burial team. The body of the source patient was placed in a body bag, the grounds were sprayed by the attendants, and the husband and body were transported in their vehicle to the mother’s home. A safe burial was performed immediately, followed by a wake inside the mother’s home. Postmortem oral swabbing was not performed.

Husband On 22 October, four days after the death of the source patient, the husband (aged 40 years) experienced onset of fever, weakness, and myalgia. He purchased gentamicin from a village trader and took the remainder of his wife’s medications for malaria and typhoid fever. On 26 October, the husband began to have vomiting and diarrhea. He remained self-sequestered in his home with no contact with family members, but he ran into the palm forest directly behind his home when the DHN made surveillance visits. On 29 October, due to a worsening of his condition, the husband agreed to be transported by ambulance to the isolation unit in Tubmanburg. He was confirmed EBOV-positive on 6 November and discharged on 21 November.

Mother On 26 October, 8 days after the death of the source patient, the mother (aged 53 years) had onset of both diarrhea and ‘heavy weakness’ without fever. She denied having symptoms during surveillance visits by the DHN. She remained alone in her home until her admission to the Tubmanburg isolation unit 7 November. She was confirmed to be EBOV-positive and discharged on 23 November.

Figure 12. Evolution of the Familial Cluster of Ebola Virus Disease: Disease Progression and Events Abbreviations: CCC, community care center; DHC, district health clinic; sx, symptomatic.

Sister and Niece On 5 November, the sister (aged 38 years) presented to the DHC (Figure 12) with fever, abdominal pain, vomiting, diarrhea, subconjunctival hemorrhage, and inability to walk. She refused ambulance transport to the isolation center. On 6 November, the DHN and the DHO brought an ambulance to her home, where the niece (aged 23 years) reported a headache, anorexia, chills, and weakness. Both were transported to the isolation center. The sister died on 8 November. The niece progressed to wet symptoms, recovered, and was discharged on 23 November.

Brother-in-Law On 23 October, five days after the death of the source patient, the brother-in-law (aged 33 years), had onset of fever, headache, and weakness. He remained in the presence of his family until 27 October, when he experienced vomiting and diarrhea. He isolated himself within a bedroom without contact with his family. On 30 October, he self-presented to the isolation unit and died on 3 November.

Clinical Course

We resolved the chain of transmission of one source patient and five secondary cases with dates of symptom onset and clinical outcome (Figure 15). The source patient died in the community and is classified as a probable case; the five secondary cases, who were each admitted to the isolation unit, were laboratory-confirmed as positive for EBOV. Contact tracing identified no additional cases of secondary transmission or tertiary cases, including the taxi driver from the day the source patient died.

The median age was 39 years (range 23-53); three (50%) were female (Tables 14 and 16). All six cases had direct physical contact with the source patient or her body fluids. The most common mechanism of contact was through bedside care, which accounted for transmission in three cases (50%). One case (17%) had contact only with soiled linens and body fluids, and one case (17%) had only skin-to-skin contact.

Clinical features (Table 14 and 16) at presentation were nonspecific and included weakness or profound weakness in six cases (100%), fever and headache in four cases (67%), myalgia in three cases (50%), anorexia in two cases (33%), and one case each had chills (17%) and conjunctivitis (17%). In the wet phase of disease, all six cases (100%) had diarrhea, five (83%) had vomiting, three (50%) had subconjunctival hemorrhage, two (33%) had abdominal/epigastric pain, and one case each had diaphoresis (17%), melena (17%), and muscular rigidity (17%). Evidence for hemorrhage was available for three cases (50%), of which two were decedents. The clinical course of the brother-in- law in the isolation unit was relayed by the husband without dates of onset.

Epidemic Transmission Parameters

The incubation period is used for monitoring contacts, isolation and quarantine, and for determining a region to be free of transmission following an outbreak.17 The median incubation period for the six cases was 6.5 days (range 4-14 days). For survivors, the median incubation period was 8 days (range

8-14 days), possibly suggesting an association between slower disease progression and survival. For the three decedents, the incubation period was 4 days (range 4-5 days) (Table 14). For single- and multi-day exposures, the median incubation periods were 5 days (range 4-8 days) and 11 days (range 8-14 days), respectively. The COSI was 21 days (range 20-24 days), which highlights the importance of the 42-day interval used by WHO prior to declaring an end to transmission. 17

Table 14. Demographics and Frequency of Clinical Symptoms, by Dry and Wet Phases of Illness

Abbreviation: n = number of observations.

The infectious period is a risk measure of secondary transmission. We calculated the infectious period from onset of wet symptoms to survival or death. The median infectious period was 11 days (range 7-32 days) (Tables 15 and 17). The infectious period could not be determined for the niece, because we could not determine the date of wet-symptom onset.

Table 15. Epidemic Transmission Parameter Estimates, Ebola Virus Disease Familial Cluster

*Calculated for the five secondary cases.

Onset to clinical assessment (Tables 15 and 17) is an indicator of how early in disease (i.e., time interval following symptom-onset) a potentially infectious person is identified in the community for intervention. For the five cases assessed by the DHN, the median onset to clinical assessment was 17 days (range 3-22 days). The source patient was the first to seek care, which occurred on her third day of symptoms (3 October). Active case monitoring by the DHN was initiated on 17 October, when the source patient returned to the area from Monrovia. The mother, niece, and sister were initially assessed at 20, 21, and 22 days, respectively, post-symptom onset. For the three survivors, the median number of days from hospitalization to discharge was 18 (range 17-24 days). Three patients died (CFR 50%). The source patient died in the community after an 18-day infectious period. The median number of days from hospitalization to death was 15 (range 12-18 days) for the two secondary cases that died in isolation.

DISCUSSION

Epidemic Response in Liberia

With the WHO declaration of a public health emergency of 8 August 2014, the Armed Forces of Liberia established checkpoints to restrict the movement into and out of affected regions.18 Coincident

with the arrival of WHO to the region, the Ebola Response Incident Management System identified isolation of persons with EVD as the ‘immediate and overriding objective’ of all response activities in Liberia.19 From there, the international response was structured to address the Four Pillars19: (1) early detection, isolation, and treatment; (2) safe patient transport; (3) safe and dignified burial; and (4) IPC. Mobilization of district-level surveillance, both within the community and at road checkpoints, proved essential for identifying sick persons and maintaining awareness of travel into and out of the Bomi County. The source patient made three separate trips to Monrovia during this time period, each of which was by public taxi. First, on 27 September, she traveled to attend the Lofa market in the Red Light District, followed by the second trip on 15 October to seek care at a private clinic. Her third trip was to seek admission to the isolation center in Tubmanburg. The husband recounted their travel southwest from their village, back to and through Monrovia, and then northeast, because they wanted to avoid temperature checkpoints stationed along the main larger roads.

Isolation and Bed Capacity In March 2014, a holding unit was erected adjacent to the county hospital in Tubmanburg with the purpose of isolating symptomatic patients and then transporting infected patients to the MSF ETU in Monrovia.20 In July, however, concern over HCW infections and the lack of IPC within the holding unit led to its closure, and closes of the county hospital and all 23 clinics in the Bomi County Community Health Department.20 Then, on August 18 and October 9, two new isolation units opened again in Tubmanburg adjacent to the county hospital. Treatment was limited to oral hydration solution, and nurses entered only twice daily to carry in the food dropped off by family members. The only diagnostic laboratory was in Monrovia at the MSF ETU, and transport of blood samples and results required a motorcycle courier to make the two-hour drive to the laboratory. Patients often remained inside the holding unit for days without knowing their infection status, while being exposed to patients with EVD.

Between July and December, 2014, 93% of secondary cases were generated by patients who died in the community, compared to only 7% from patients who died inside an ETU.14 The failure of the WHO to respond rapidly to the epidemic allowed unchecked transmission throughout the affected countries.21 While the bed capacity expanded greatly in latter 2014 and into 2015, many treated only one or two patients, while others were fully constructed but never commissioned for use.19,22

Surveillance and Community Resistance Initial recognition by the DHN of illness in the source patient would prove critical, because the time spent in the community beyond the fourth day of illness presents the greatest—and increasing—risk for community transmission.23 The source patient sought care early in her illness (onset to clinical assessment=3 days). Had an isolation bed been available, the five cases of secondary transmission could have been prevented. However, repeated attempts to isolate cases, however, were frustrated not only by the lack of isolation beds, but later by the abject refusal of the family to ride in an ambulance. Despite decades of response to smaller VHF outbreaks in Central Africa, and the identification of key elements of a response, the West Africa epidemic again highlighted a gap in operationalizing a large- scale response: the fundamental importance of establishing a relationship with the communities

concerned and the public at large.24 Missteps early in the epidemic, in which teams were forcibly removing children and family members from their homes, contravened closely-held cultural beliefs in honoring the death with burial rites.5,25 Among impoverished populations scarred by histories of colonialization and civil war, a disbelief in the existence of ‘Ebola’ thwarted both local and international efforts to control the outbreak (Brown H). Notably, the DHN and DHO, trusted members of the community, ended the chain of transmission by bringing the ambulance directly to the homes of the husband, mother, sister, and niece (Figure 13). A flattening of hierarchy24 was not understood by leaders of the Ebola response, who had encouraged enforcement rather than cooperation to control transmission.6

Social Distancing Children were not among the secondary cases in this cluster, due to cultural practices of not including them in the care of sick family members24,26 and self-sequestration by the cases, noted by Faye et al.9 as an effective strategy that contributed to interrupting transmission in Guinea. Social distancing (e.g., ‘no-touch’ policies), curfews, and prohibitions on large gatherings were announced by radio.12,19 This influenced the source patient, husband, and brother-in-law to self-sequester and have no physical contact with family. Of note, the inability to find treatment was a risk factor for transmission in our study, particularly the travel and three-day stay of the source patient and mother at the private clinic in Monrovia. PPE was not widely available in Liberia prior to the epidemic, and clinic staff, 63

desperately under-resourced, used the same pair of gloves to treat multiple patients, likely an attempt to maintain a barrier between themselves and the many patients under their care. We cannot rule out the clinic as the source of infection for the husband and mother, nor could we determine if the source patient infected other patients or family members in the clinic. If they were exposed to virus in the clinic, however, it likely superimposed latent infections from the care provided to the source patient once she progressed to the wet phase of EVD.

Exposures and Transmission

Although we cannot exclude the possibility of EBOV transmission from sources not disclosed or discovered, our extensive efforts to precisely identify the time and place of transmission failed to uncover other sources of transmission for the source patient or secondary cases than those proposed. As a schoolteacher, the brother-in-law was at home because of school closures, and there were no known sick persons in his hometown of Tubmanburg. We were unable to obtain information on his travel history. The index case and husband could not identify a known exposure event to which they could attribute her illness. Without a risk history, and in combination with the negative diagnosis received at the Monrovia clinic, they attributed her illness to another etiology. Interestingly, the husband stated that people in their village did not believe the virus could reach their community, despite its close proximity to Monrovia. It is important to note that at this time, ‘Ebola’ was synonymous with bleeding,5 as it was in the West. Contact with a bleeding person would be subject to recall, but contact with a sick person who appeared weak and sweating would not be an unusual event during the Liberian rainy season, particularly given the background burden of disease from malaria and other mosquito-borne diseases.

We considered the epidemiologic link of the source patient to Lofa County, known for their prized production of palm oil. Palm oil and the fruits were regularly purchased by the source patient. Palm plantations have been the setting of previous outbreaks in the DRC,27 as some bat species roost in the trees for fruit. The possibility of fallen fruits contaminated with bat saliva or guano was explored by Leroy et al. (2009),27 and has been proposed as a potential source of spillover of ebolaviruses from frugivorous bats to NHPs and humans.28 Recently, EBOV RNA was isolated from a bat (Miniopterus inflatus) caught in Nimba County, Liberia,29 a straight-line distance of 50 miles from Guéckédou, Guinea.

Our findings raise an important question about the role of sweat in EBOV transmission. Zaki et al.30 demonstrated the presence of EBOV and antigen within and around sweat glands in 14 of 14 axillae and neck biopsies. Based on those findings, they proposed that skin may service as a site of viral replication, not merely viral secretion. Sweat was identified as the mode of transmission between an infected father with only mild illness to his infant during a three-hour walk bush walk.27 Dowell et al.31 (1999) concluded sweat-based transmission was an ‘intriguing explanation’ for transmission events for which no risk factor could be identified. Based on our findings following exhaustive efforts to identify other modes of transmission or exposures not previously accounted for, we hypothesize that the source patient and brother-in-law were infected by skin-to-skin contact with the source patient, in

which sweat was transferred from an infected person to a susceptible person. We support the proposal of Zaki et al.30 to establish a skin surveillance system to explore the role of direct physical contact in transmission of the virus.

Infection of expatriate humanitarian workers provided the opportunity for real-time monitoring of EBOV viral loads in tissues and fluids in patients undergoing treatment in high-containment biofacilities in the US and Europe. In two patients, EBOV was detected in axillary sweat on day 24 and from axillary, inguinal, and forehead sweat from during days 20-40 of EVD.32 Moreover, anecdotal reports of skin-to-skin contact also implicate sweat as the mode of transmission, including as the source of the virus for the Liberian man who died of EVD in Dallas.33 Prior to his death, he recalled that his only contact with a sick person was when he assisted a sick pregnant woman into a taxi the week before leaving for the US. In 2015 in the Red Light District in Monrovia, two cases of EVD were diagnosed in two people who helped a sick person into a taxi.34

The growing evidence of sweat in the epidemiology of EVD warrants further examination as an underrecognized mode of transmission, particularly for outbreaks in densely-populated urban environments of tropical Africa. Risk communication, and the promotion of social distancing, should include an awareness of skin-to-skin contact—not just contact with blood, stool, and vomitus—as opportunities for transmission.

CONCLUSION

The time, setting, and mechanism of EBOV transmission events are key observations upon which an epidemic response is operationalized. Reconstruction of chains of transmission provides the opportunity to examine person-level transmission and assess the effectiveness of control measures. We described the key transmission events that contributed to secondary transmission, and determined that the lack of isolation beds, coupled with fear and mistrust, contributed to secondary transmission to five family members of the source patient. Importantly, self-sequestration by three of the cases prevented additional secondary prevention and generated no cases of tertiary transmission. This was particularly important given the household setting and the presence of children.

After exhaustive efforts to identify a source of EBOV transmission to the source patient and the brother-in-law, we excluded all modes and sources of transmission except sweat. This is an important finding with implications for driving ongoing and unrecognized virus transmission. Risk communications for social distancing should be instituted immediately in future outbreaks. Our data join a limited number of descriptive studies in which single-day transmission events, and the precise dates of clinical onset, are documented. Together, our data provide estimates of epidemic transmission parameters with the greatest precision possible from an epidemic context. Our informed estimates will better characterize the epidemiology of EVD, improve spatiotemporal modelling of EBOV transmission and infection dynamics, and guide resource allocation during future VHF epidemics.

Figure 14. Ebola Virus Disease Familial Cluster: Transmission Events, Symptom Onset, and Clinical Outcomes

Table 16. Demographic Characteristics, Transmission Events, Symptoms, and Outcomes, EVD Cluster

*Less than 24 hours of exposure to the source patient.

Table 17. Estimations of Case-Specific Incubation and Infectious Periods

*Less than 24 hours of exposure to the source patient.

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13. Blackely DJ, Lindblade KA, Kateh F, et al. Rapid intervention to reduce Ebola transmission in a remote village–Gbarpolu County, Liberia, 2014. MMWR Morb Mortal Wkly Rep. 2015;64:175– 178.

14. Lindblade KA, Kateh F, Nagbe TK, et al. Decreased Ebola transmission after rapid response to outbreaks in remote areas, Liberia, 2014. Emerg Infect. Dis. 2015;21(10):1800–1807.

15. World Health Organization. Ebola situation report–1 October 2014. https://apps.who.int/iris/

bitstream/handle/10665/135600/roadmapsitrep_1Oct2014_eng.pdf?sequence=1.Published October 1, 2014. Accessed March 20, 2019.

16. 2014 Ebola Outbreak in West Africa Epidemic Curves. Centers for Disease Control and Prevention Web site. https://www.cdc.gov/vhf/ebola/history/2014–2016–outbreak/cumulative– cases–graphs.html. Updated April 3, 2019. Accessed May 7, 2019.

17. World Health Organization Ebola Response Team. Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections. N Engl J Med. 2014;371:1481–1495.

18. The Bureau of Public Affairs. Daily Media Summary. Minist Foreign Aff Repub Lib. (2014); available at http://mofa.gov.lr/public2/2press.php?news_id=1226&related=7&pg=sp&sub=178. Accessed 26 January 2019.

19. Nyenswah TG, Kateh F, Bawo L, et al. Ebola and its control in Liberia. Emerg Infect Dis. 2016;22(2):169–177.

20. Logan G, Vora NM, Nyensuah TG, et al. Establishment of a community care center for isolation and management of Ebola patients—Bomi County, Liberia, October 2014. MMWR Morb Mortal Wkly Rep. 2014;63(44):1010–1012.

21. Townsend JP, Skrip LA, Galvani AP. Impact of bed capacity on spatiotemporal shifts in Ebola transmission. PNAS. 2015;112(46):14125–14126.

22. Coltart CEM, Lindsey B, Ghinai I, et al. The Ebola outbreak, 2013–2016: old lessons for new epidemics. Phil Trans R Soc B. 2017;372(1721):20160297.

23. Yamin D, Gertler S, Ndeff–Mbah ML, et al. Effect of Ebola progression on transmission and control in Liberia. Ann Intern Med. 2015;162(1):11–17.

24. Borchert M, Mutyaba I, Van Kerkhove MD, et al. Ebola hemorrhagic fever outbreak in Masindi District, Uganda: outbreak description and lessons learned. BMC Infect Dis. 2011;11:357–373.

25. Kobayashi M, Beer KD, Bjork A, et al. Community knowledge, attitudes, and practices regarding Ebola virus disease—five counties, Liberia, September–October, 2014. MMWR Morb Mortal Wkly Rep. 2015;64(26):714–718.

26. Dowell SF. Ebola hemorrhagic fever: why were children spared? Pediatr Infect Dis J.1996;15(3): 189–191.

27. Leroy EM, Epelboin A, Mondonge V, et al. Human Ebola outbreak resulting from direct exposure to fruit bats in Luebo, Democratic Republic of Congo, 2007. Vector Borne and Zoonotic Dis. 2009;9(6):723–728.

28. Spengler JR, Ervin ED, Towner JS, et al. Perspectives on West Africa Ebola virus disease outbreak, 2013–2016. Emerg Infect Dis. 2016;22(6):956–973.

29. Kupferschmidt K. This bat species may be the source of the Ebola epidemic that killed more than 11,000 people in West Africa. Science. January 24, 2019. doi:10.1126/science.aaw7864. Accessed March 21, 2019.

30. Zaki SR, Shieh WJ, Greer PW, et al. A novel immunohistochemical assay for the detection of Ebola virus in skin: implications for diagnosis, spread, and surveillance of Ebola hemorrhagic fever. J Infect Dis. 1999;179(suppl 1):S36–S47.

31. Dowell SF, Mukunu R, Ksiazek TG, et al. Transmission of Ebola hemorrhagic fever: a study of risk factors in family members, Kikwit, Democratic Republic of the Congo, 1995. J Infect Dis. 1999; 179(suppl 1):S87–S91.

32. Petrosillo N, Nicastri E, Lanini S, et al. Ebola virus disease complicated with viral interstitial pneumonia: a case report. BMC Infect Dis. 2015;15:432.

33. Troh L, Wicker C. My Spirit Took You In: The Romance that Sparked an Epidemic of Fear: A Memoir of the Life and Death of Thomas Eric Duncan, America's First Ebola Victim. 1st ed. New York, NY: Weinstein Books; 2015.

34. Nyenswah TG, Fallah M, Sieh S, et al. Controlling the last known cluster of Ebola virus disease— Liberia, January–February, 2015. MMWR Morb Mortal Wkly Rep. 2015;64(18):500–504.

CHAPTER IV CLINICAL SEQUELAE AND FUNCTIONAL DECLINE AMONG SURVIVORS OF EBOLA VIRUS DISEASE, BOMI COUNTY, LIBERIA

The West Africa EVD epidemic, unexpected and protracted, heralded a new era of ebolavirus epidemiology. No longer a disease of hemorrhage and death, more than 17,000 people survived infection with a virus once considered to be nearly always fatal.1 Post-Ebola sequelae were first described following the 1976 SUDV outbreaks.2 Among survivors, a constellation of consistent, frequent, and debilitating symptoms, which encompass a spectrum of non-descript clinical sequelae of poorly understood pathogenesis without prescriptive treatments. Designated as post-Ebola virus disease syndrome (PEVDS),3 arthralgias, disabling fatigue, ocular complications, hearing loss, and neuropsychiatric symptoms are among the most commonly-reported sequelae, some persisting more than 20 years after infection.4-6 PEVDS presents a challenge to individual and national recovery, with a continued threat to global health security. The consequences of lost productivity, food insecurity, and the breakdown of cohesive community structures has the potential to maintain the enfeebled state of the affected countries, creating and maintaining large regions of susceptibility to future emergence events.

We conducted physical examinations and evaluations of functional status to investigate the clinical symptoms reported by three survivors from the transmission chain described in Chapter III. Our aim was to characterize, as fully as possible, and within the limitations of ongoing EBOV transmission, the physical and psychiatric symptoms, patterns of distribution by body system, and impact on longitudinal regain-of-function or increasing disability. We further present proposed mechanisms of pathophysiology with results of advancing studies conducted in the wake of the epidemic.

MATERIALS AND METHODS

Ethics Approval

All interviews and physical examination were conducted in the home village of each participant while active EBOV transmission was occurring in Liberia, and therefore occurred during the international Ebola emergency response. Data collection conformed to all relevant General Principles of the Declaration of Helsinki.7 Human subjects approval was granted by the LSU IRB (Appendix J).

Case Ascertainment and Informed Consent

The survivors presented in this case series were patients discharged from the ETU where the investigators provided direct patient care for EBOV-infected persons, and then participated health- system strengthening and community reintegration efforts. During June and July 2015, the

investigators resided in a rural village approximately 15 km northeast of Monrovia, the capital city of Liberia, to investigate the events of EBOV transmission and post-Ebola sequelae. Survivors and family members were informed of the purpose of the study, that their privacy would be protected, and that the findings would be shared with the international community to aid in understanding how EBOV is transmitted among family and community members, and to understand the symptoms associated with PEVDS. Written informed consent was obtained and witnessed by a third party. Cases were interviewed in their homes alone or in the presence of family and community members, as per choice.

Data Collection and Analyses

Definitions A survivor was defined as a laboratory-confirmed case of EVD who was admitted to the Bomi County ETU, recovered from EVD, and discharged to their home community. Discharge from the ETU was based on two criteria: (1) serial laboratory-confirmed EBOV-negative blood samples, and (2) resolution of EVD-associated symptoms (e.g., afebrile, cessation of diarrhea). An additional criterion was the ability to perform self-care within their homes.

Interviews Following informed consent, each survivor was interviewed and examined in their homes in English using an open-ended format (Appendix L).8 Survivors were asked about their activities, exposures and knowledge of EVD prior to disease onset and throughout the course of illness into the convalescent period. Initial (t1) interviews and physical exams occurred in February 2015 (90 days post-discharge). Secondary interviews (t2) and physical exams took place in June 2015 (120 days post-discharge). We dichotomized the convalescent period as acute (i.e., discharge to <90 days) and subacute (i.e., >90 days after discharge).

Physical Examination Data collection was performed by one investigator, who is a clinician and infectious disease epidemiologist with experience in West African cultures. Vital signs (i.e., blood pressure, heart rate, pulse oximetry, temperature) were obtained using portable durable medical equipment. The physical examination conformed to the methodology outlined by Campbell et al.,9 including 18-tender point exam, utilized by the American College of Rheumatology for assessment of symmetrical pain distribution.10 Direct physical examination of the urogenital and reproductive systems was deferred but remained part of the oral interrogation (Appendix M).

Patient-Specific Functional Scale We selected the Patient-Specific Functional Scale11 (Appendix C), used previously in a Guinean survivor cohort,12 to obtain longitudinal global functional changes. The instrument requires participants to identify high-priority activities essential for daily living, and to rate their ability to perform the activities over a specified internal of time, based on a scale of 0 to 10 (i.e., ‘0’ corresponds to wholly unable to perform the activity, ‘10’ corresponds to being fully able to perform the activity. At 90 days post-discharge (t1), survivors were instructed to rate their loss or gain in functional ability

based on their pre-EVD ability (t0). At 120 days post-discharge (t2), survivors were instructed to rate gains or losses in their functional ability based on their abilities at t1. An incremental change, from 23, for example, represents a 10% gain of function; a 31 rating represents a 20% loss of function. Final scores were calculated as percentages of recovery based. For example, a premorbid score of ‘10’ for four activities corresponds to a total possible score of 40. The functional ratings of each activity for t1 were added together, divided by 40, and multiplied by 100 to score the functional ability in the acute and subacute phases.11

RESULTS

A full history and physical examination for each survivor is detailed in Table 18. The mother is the grandmother of the niece, and the husband is the surviving spouse of the mother’s daughter, the source patient for their infections. The median age of the survivors was 40 years (range 23-53 years). None of the survivors had a history of chronic disease. The mother was born with bilateral exotropia that had not affected her vision. All three survivors experienced weight loss as a result of their disease. This was least extreme in the 23-year-old and most profound in the 53-year-old, who, despite a normal BMI, appeared cachectic and weak.

Categories of Disease Severity

We classified the severity of EVD as mild, moderate, and severe, based on their clinical manifestations during their admission to the ETU. Mild disease was unexpectedly observed in the 53-year-old mother, who was afebrile, had simultaneous onset of diarrhea and profound weakness, and did not manifest signs or symptoms of hemorrhage. The niece was classified as having moderate disease, based on her fluid losses from diarrhea and vomiting and conjunctivitis. The husband was admitted with signs of hemorrhage and was classified as having severe disease.

Musculoskeletal Pain

Each of the three survivors identified pain as the greatest obstacle to performing activities of daily living, reintegrating with the community, earning an income, and farming for food. In all cases, pain was symmetrical and mechanical, defined by: (1) pain with activity and improvement with rest, (2) no pain during the night, and (3) morning stiffness and pain <30 minutes duration .13 Arthralgias were persistent and polyarticular of both large and small joints. Direct physical examination of the joints and skin were without evidence of an inflammatory process.

Despite having mild disease, the mother demonstrated the most extensive profile of severe pain. Results of the 18 tender-point assessment10 demonstrated severe, symmetrical pain at each of the 18 sites (Figure 15). She had marked guarding and withdrawal to palpation of the joints. She unable to rotate her head (i.e., horizontal cervical rotation) >45 degrees in either direction. She could not form a closed first with either hand.

Table 18. History and Physical Examination Summaries, EVD Survivors

Abbreviations: BMI, body mass index; BP, blood pressure; cm, centimeters; H, height; HR heart rate; kg, kilogram; LROM, left range of motion; O2 sat, oxygen saturation in blood; OD, right eye; OS, left eye; ROM, range of motion; T, temperature; W, weight; WNL, within normal limits. 75

The niece, whose disease would be classified as moderate, described severe bilateral myalgia in the recti femorus, vasti lateralis, and vasti medius. She recounted pain of such severity that she was unable to walk her usual distances between villages to sell items for an income. Finally, the husband, who had severe disease, described articular pain severe in the morning and moderate during the day. Pain was localized to the cervical spine and symmetrically distributed among the glenohumeral joints; tibiofemoral joints; and the distal, medial, and proximal phalanges of the hands and feet. Sagittal cervical rotation was limited to <30 degrees in flexion and extension. The glenohumeral joints were limited at 90 degrees for vertical flexion and abduction. The tibiofemoral joints full flexion and extension but with pain. Pain of the phalanges of the hands and feet was described as moderate and relieved with motion.

Neuropsychiatric Sequelae

The most severe neurologic symptom was reported by the niece (moderate disease), who reported persistent headaches that began after discharge from the ETU and continue more than two days each. The severity of the headaches left her bed-bound and unable to perform any activity of daily living.

Assessment of anxiety and depression in the mother and niece was limited to their willingness to discuss this type of symptom. The district health nurse characterized the mother as ‘acting depressed’ since her daughter died in October 2014. The husband described an inability to fall asleep and stay asleep. He said he has ‘racing thoughts’ and ‘feels grief’ over his wife’s death. He stated he was shunned by friends and community members upon his return from the ETU who refused to visit him or shake his hand. He said he felt ‘no reason to live’ upon arriving home and discovering the contents of his home had been burned.

Opthalmic Sequelae

The mother, who had mild EVD, and the husband, who had more severe EVD, both reported opthalmic sequelae. The mother described photophobia during peak daylight hours that require her to remain indoors. The husband described changes in visual acuity changes, pain, and hyperlacrimation of the left eye during his ETU admission, which worsened after discharge. In the acute recovery phase, he had complete loss of vision and continuous hyperlacrimation. Subacutely, he regained vision but with a deficit of acuity that prevented him from performing community construction activities. In the physical examination, he had evidence of hyperlacrimation and demonstrated an inability to discern at a distance of 0.3 meters. In confrontational visual field testing, he demonstrated peripheral deficits of the left eye (Table 18, Appendix M).

Functional Assessments

The essential activities for daily living are listed in Table 19. Survivors were asked to associate a clinical symptom with their inability to perform their identified activities. Functional limitations among the three survivors were predominantly associated with arthralgias and myalgias. None of the survivors identified a psychiatric symptom as impeding their functional abilities, although the husband did report insomnia and nightmares about the loss of his wife and his time in the isolation unit.

Despite having mild disease, the mother demonstrated the most marked loss of function. Her premorbid function was rated at 100%, followed by 0% in the acute phase (t1) and 10% in the subacute phase (t2). She identified arthralgia as the reason for her loss of function, followed by fatigue and myalgia as secondary factors. She described an inability to hold farming tools due to pain and weakness of both hands. She was unable to make a fist and could not hold a knife or ladle to prepare food. On the days when her arthralgia was less severe, fatigue limited her functional ability.

The niece, who had moderate disease, had acute loss of function similar to the husband, 25% and 28%, respectively, but her subacute score was 53%, compared to 43% for the husband. She identified myalgia of the bilateral quadriceps muscles as the primary reason for functional decline, followed by fatigue and persist, debilitating headaches. In contrast, the husband attributed his function losses to an inability to see in his left eye, which improved from 0% in the acute recovery phase to 20% in the subacute recovery phase. Fatigue and weakness were prevented him from walking and farming, while arthralgias and loss of vision impeded his participation in community construction activities.

Table 19. Functional Assessments Using the Patient-Specific Functional Scale

90 120 Pre-EVD Days Days

t 1 t 2 t 3 Limiting Symptoms Body System

Husband Fatigue Farming 10 3 4 Musculoskeletal Weakness

Seeing, OS 10 0 0 Vision loss Neuro-ocular

Fatigue Walking 10 3 6 Musculoskeletal Weakness

Arthralgia Musculoskeletal Construction 10 5 5 Visual acuity Neuro-ocular

Mother Cooking 10 0 1 Arthralgia Musculoskeletal

Washing 10 0 0 Arthralgia Musculoskeletal

Arthralgia Farming 10 0 0 Fatigue Musculoskeletal Myalgia

Arthralgia Bringing water 10 0 0 Fatigue Musculoskeletal Myalgia

Niece Myalgia Central Nervous System Walking 10 2 5 Headache Musculoskeletal

Cooking 10 3 7 Arthralgia Musculoskeletal

Washing 10 4 6 Arthralgia Musculoskeletal

Selling 10 1 3 Myalgia Musculoskeletal

Abbreviations: OS, left eye; t, time point.

DISCUSSION

Following the 1995 outbreak in Kikwit, DRC, survivors reported arthralgias, myalgias, abdominal pain, hearing loss, extreme fatigue, and anorexia up to six months after the outbreak.6 At 21 months after the same outbreak, more than 60% of the survivors continued to experience symmetric 78

polyarthropathies and myalgias.6 A more recent study conducted 22 years after the outbreak detected abnormal neurological exams, lower cognitive scores, and depression in survivors compared to their household controls.14 High viral titers cause sustained immune activation, with cell and tissue damage secondary to apoptosis and necrosis.15 These pathophysiologic processes have been postulated as the biologic bases of both the development and severity of PEVDS, but a causal mechanism or biomarker has yet to be identified.1,16

Musculoskeletal Sequelae

Arthralgia and myalgia constitute the majority of reported complaints of pain and disability among survivors of the West Africa epidemic.13,17 Remarkably, the same symptom profile of arthralgias and large-muscle myalgias has been reported with consistence by survivors across all outbreaks of ebolaviruses.4,18,19 Arthralgia alone has been documented in up to 87% of survivors across three cohort studies.12,20,21 In nearly all cases, the patterns of arthralgia are symmetrical and involve multiple small and large joints. We observed the same pattern of symmetric pain distribution in the mother, whose 18 tender-point exam was positive at each of the 18 examined joints and, to a lesser extent, in the husband, who experienced severe cervical arthralgia and symmetrical arthralgia of the patellofemoral joints. Both had disabling pain that limited their cervical range of motion.

Two hypotheses have been proposed to explain the underlying pathophysiology of PEVDS. The first is that patients with high viral loads during the course of illness have persistence of RNA within immune privileged sites.22 This first hypothesis is contradicted by the recent finding that viral load on admission was not predictive of symptom frequency nor severity.17 A second hypothesis, which is more consistent with the distribution of symptoms both in the body and in those with less severe disease, is that EVD results in an overwhelming immune activation, including the destruction of cells and tissues that sustain damage from inflammatory cytokines.21,23 Other causalities have been proposed. In an animal study, double-stranded DNA, which is released from cells during destruction of tissues (e.g., rhabdomyolysis), and the presence of heat shock proteins in the synovium were associated with higher titers of autoantibodies.24 However, the underlying pathophysiology of musculoskeletal complaints in survivors remains unknown.

Neuropsychiatric Sequelae

Quiescent infection, recurrent neurologic disease, and post-EVD sequelae demonstrate an emerging range of neurovirulence and incomplete immune privilege of the CNS against EBOV.25 EBOV RNA has been detected in CSF25,26 and ocular fluid.27 Headaches28,29; short-term memory impairment20; tinnitus and hearing loss4,6,25; altered sense of taste and smell12; and confusion and hallucinations have been documented in survivors, with the most disabling symptoms noted in patients known to have had severe EVD.30

Neurologists working with survivor cohorts have diagnosed localized deficits, such as homonymous hemianopia, tremors, gait abnormalities, muscle laxity, abnormal reflexes, and asthenias, which are

generally observed in post-stroke patients.30,31 Computed tomography imaging in Sierra Leone32 demonstrated inflammatory changes, including middle cerebral artery gliosis25; meningoencephalitis26; ischemic changes26,33; atrophy of the cerebellar, parietal, and temporal lobes; and ventriculomegaly and hemorrhagic encephalitis.32 Each of these findings has the potential to be profoundly disabling. The broad range of neurosequelae cannot be attributed to one pathology. Viral persistence within the CNS, post-inflammatory nerve damage, and observational associations of altered consciousness, hemodynamic collapse, and cerebral hypoperfusion have been suggested.31

Psychiatric Sequelae Chronic pain contributes to the frequency and severity of anxiety,34 a finding upheld by the World Mental Health survey, which found an association between the number of painful body sites and generalized anxiety disorder.35 In post-epidemic studies, computed tomography imaging detected evidence of atrophy in the cerebellar, parietal, and temporal lobes, all with the potential to be profoundly disabling and contributory to a depressive affect.32 Depression, anxiety, insomnia, grief, and fear are frequently reported by survivors who experience social isolation due to an inability to work and contribute to their communities.1,4,12,29,36,37 Finally, the history of the region cannot be overlooked. Superimposed on the traumas from decades of civil war, the prolonged and chaotic nature of the epidemic and loss of family and community members are certain to compound—and confound— post-EVD sequelae and recovery.

Opthalmic Sequelae

The complexity of diagnosing opthalmic pathology lies in the requirements for visualization of the external and internal structures of the eye. Identification of lesions or structural abnormalities requires not only a clinical specialist, but advanced equipment and drugs. In African survivors, lesions of the retinal structures and along the axonal body of the optic nerve have been described,27,38 which are consistent with the development of conjunctivitis, uveitis, hyperlacrimation, and loss of visual acuity.5,22,39 In a Sierra Leonean cohort, 60% of survivors reported ocular symptoms at 121 days after discharge,21,31 and nearly 20% of survivors in West Africa experience vision problems.40 EBOV is neuroinvasive and causes widespread CNS inflammation. No conclusive evidence exists to determine the cause of ocular symptoms. Whether due to RNA persistence within the ocular chamber, or are the consequences of infection and inflammation, intermediate treatments and long-range therapies will be required to address opthalmic sequelae associated with EBOV survivorship.

Limitations

Assessment of physical symptoms is subject to bias on the part of the patient and the investigator. The interpretation of pain and disability involve personal perceptions of health and, therefore, are subject to inherent cultural biases. Our unique approach of residing in the village with the survivors was our attempt to overcome these biases by directly observing the context in which they experienced and preformed their activities of daily living.

Due to the emergency nature of the response, we were unable to perform clinical laboratory analyses to identify elevations in inflammatory biomarkers or cellular suggestive of immune-mediated disease processes. Additionally, we did not have access to the Ct values of the survivors from which to infer the association between viral load, EVD severity, and the development of symptoms. However, a recent finding from Wing, et al.17 found no association of viral load with development of PEVDS.

The Research Agenda for Post-Ebola Virus Disease Syndrome

Longitudinal Cohort Studies The largest ever number of survivors from the West Africa epidemic was absolutely unanticipated at the outset of the epidemic, and the global cohort of survivorship will continue to expand as outbreaks of ebolavirus grow larger and more frequent. Our understanding of EVD is incomplete but our knowledge is evolving. Concerted efforts are underway in each of the West African countries to describe the syndrome, determine its causality, and identify treatment modalities. Areas of active investigation into the etiology(ies) of PEVDS include the role of global immune activation in cellular and tissue damage, the deposition of immune complexes in large and small joints, and the ability of EBOV to persist in immune-protected body sites, include the central nervous system. In Guinea, the French-led PostEboGui Study, a prospective, multidisciplinary cohort study, is investigating the long- term clinical, psychosocial, and viral outcomes of survivorship.13,29,41 Public Health England and the UK Ministry of Defence are operating survivor clinics throughout Sierra Leone, in addition to clinical trials to assess the safety and efficacy of favipiravir for the clearance of EBOV RNA from immune- privileged sites,42 and the use of convalescent plasma for treatment of PEVDS.43 The National Institute of Allergy and Infectious Diseases initiated the PREVAIL III trial in 2015 for longitudinal studies of survivors in Liberia,44 and an additional study, PREVAIL IV, is currently enrolling male survivors to assess the ability of experimental drug GS-5734 to eliminate EBOV RNA from semen.45 Understanding disease pathogenesis and identifying acute and chronic co-morbidities (e.g., malaria; history of stroke, diabetes, exposure to heavy metals) will identify endpoints in the clinical course of EVD for administration of therapies to mitigate disease severity and post-EVD sequelae.

Therapeutics Treatment for the PEVDS is currently supportive and there is no definitive cure. Beyond recognizing the clinical features that are manifested as part of PEVDS, efforts must shift toward therapeutic measures that might be initiated during acute infection to minimize the incidence and severity symptoms among survivors. In addition, the scientific community must focus on effective therapeutic measures that may be useful in managing symptoms of PEVDS. Since the pathogenesis of the PEVDS is thought to be related to systemic injury resulting from the original infection, a sustained immune response, autoimmune-like inflammatory state, and viral persistence in specific anatomical locations (i.e., eye, brain, testes), treatment strategies directed at the inflammatory cascade and the immune system have been proposed as initial targets for new therapies.46 In 2015, the National Institute of Allergy and Infectious Diseases initiated the PREVAIL III trial47 to facilitate long-term follow-up of survivors and household contacts in Liberia. From this group, they are recruiting subjects for the PREVAIL IV trial, which will examine the ability of an experimental compound, GS-5734, to clear

persistent EBOV RNA from the semen in male survivors.45 In the immediate future, West African survivors will require an integrated system of care across the health services spectrum.

CONCLUSION

Post-Ebola virus disease syndrome is a constellation of multi-system, chronic debilitating symptoms experiences by survivors of infection with ebolaviruses, and is now reported in the majority of the 17,000 survivors of the West Africa epidemic.48 Research among EVD-infected patients and survivors reveals both acute and post-infectious complications, with no gains in defining the underlying pathogenesis. Our results provide person-level data on the experience of PEVDS, with an emphasis on the extent of disability associated with the symptoms. Together with other studies, these findings underscore the need for the international clinical and public health communities to continue investigations into the post-viral phase of infections with emerging tropical viruses. Through investment and development of health care and public health infrastructures for at-risk populations, we will expand our knowledge of EVD pathophysiology, identify therapeutic targets, and gain key insights for the continued development of vaccines.

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27. Steptoe PJ, Scott JT, Baxter JM, et al. Novel retinal lesion in Ebola survivors, Sierra Leone, 2016. Emerg Infect Dis. 2017;23(7):1102–1109.

28. Wilson HW, Amo–Addae M, Kenu E, et al. Post–Ebola syndrome among Ebola virus disease survivors in Montserrado County, Liberia 2016. Biomed Res Int. 2018;1909410. doi:10.1155/ 2018/1909410. Accessed March 30, 2019.

29. Etard JF, Sow MS, Leroy S, et al. Multidisciplinary assessment of post–Ebola sequelae in Guinea (PostEboGui): an observational cohort study. Lancet Infect Dis. 2017:17(5):545–552.

30. Bowen L, Smith B, Steinbach S, et al. Survivors of Ebola virus disease have persistent neurological deficits. Neurology. 2016;86(suppl 16):S53–003.

31. Billioux BJ, Smith B, Nath A. Neurological complications of Ebola virus infection. Neurotherapeutics. 2016;13(3):461–470.

32. Howlett PJ, Walder AR, Lisk DR, et al. Case series of severe neurologic sequelae of Ebola virus disease during epidemic, Sierra Leone. Emerg Infect Dis. 2018;24(8):1412–1421.

33. Chertow DS, Kleine C, Edwards JK, et al. Ebola virus disease inWest Africa—clinical manifestations and management. N Engl J Med. 2014;371(22):2054–2057.

34. Beesdo K, Hoyer J, Jacobi F et al. Association between generalized anxiety levels and pain in a community sample: evidence for diagnostic specificity. J Anxiety Disord. 2009;23(5):684–693.

35. Gureje O, Von Korff M, Kola L, et al. The relation between multiple pains and mental disorders: results from the World Mental Health Surveys. Pain. 2008;135(1–2):82–91.

36. Jagadesh S, Sevalie S, Fatoma R, et al. Disability among Ebola survivors and their close contacts in Sierra Leone: a retrospective case–controlled cohort study. Clin Infect Dis. 2017;66(1):131– 133.

37. Fallah MP, Prevail III Research Team. A cohort study of survivors of Ebola virus infection in Liberia (PREVAIL III). Abstract presented at: Annual Conference on Retroviruses and Opportunistic Infections; Feb 22–25, 2016; Boston, MA. http://www.croiconference.org/ sessions/cohort–study–survivors–ebola–virus–infection–liberia–prevail–iii. Accessed March 30, 2019.

38. Holland GN, Rimoin AW. The ophthalmic sequelae of Ebola. JAMA Ophthal. 2018;136(6):693– 694.

39. Moshirfar M, Fenzl CR, Li Z. What we know about ocular manifestations of Ebola. Clin Ophthalmol. 2014;8:2355–2357. doi:10.2147/OPTH.S73583. Accessed March 30, 2019.

40. Eye care for Ebola survivors [news release]. Beni, DRC: World Health Organization. April 10 2019. https://www.who.int/news–room/feature–stories/detail/eye–care–for–ebola–survivors. Accessed May 5, 2019.

41. Hereth–Hebert E, Bah MO, Etard JF, et al. Ocular complications in survivors of the Ebola outbreak in Guinea. Am J Ophthalmol. 2017;175:114–121.

42. Sissoko D, Laouenan C, Folkesson E, et al. Experimental treatment with favipiravir for Ebola virus disease (the JIKI Trial): a historically controlled, single–arm proof–of–concept trial in Guinea. PLoS Med. 2016;13(3):e1001967. doi:10.1371/journal.pmed.1001967. Accessed March 30, 2019.

43. van Griensven J, Edwards T, de Lamballerie X, et al. Evaluation of convalescent plasma for Ebola virus disease in Guinea. N Engl J Med. 2016;374:33–42.

44. Ebola virus disease survivors: clinical and immunologic follow–up. Identifier NCT02431923. https://clinicaltrials.gov/ct2/show/results/NCT02431923. Updated August 23, 2018. Accessed March 30, 2019.

45. GS–5734 to assess the antiviral activity, longer–term clearance of Ebola virus, and safety in male Ebola survivors with evidence of Ebola virus persistence in semen. Identifier NCT02818582. https://clinicaltrials.gov/ct2/show/NCT02818582 Updated January 28, 2019. Accessed March 30, 2019.

46. Malvy, D, McElroy AK, de Clerck H, Günther S, van Griensven J. Ebola virus disease. Lancet. 2019;393:936–948.

47. Study of Ebola survivors opens in Liberia: trial to examine long–term health effects of Ebola virus disease [news release]. Bethesda, MD: National Institutes of Health News Releases; June 17, 2015. https://www.nih.gov/news–events/news–releases/study–ebola–survivors–opens–liberia. Accessed March 30, 2019.

48. Bausch DG. Sequelae after Ebola virus disease: even when it’s over it’s not over. Lancet Infect Dis. 2015;15(8):865–866.

CHAPTER V SUMMARY OF RESULTS AND CONTRIBUTION TO PUBLIC HEALTH PRACTICE

SUMMARY OF RESULTS

Chapter II. Multi-Pathogen Infections of Dengue Virus

Dengue presents as an AFI, typically characterized by fever, myalgia, and headache,1 and is indistinguishable from other tropical diseases, including leptospirosis, during the early-onset of symptoms. The difficulty, therefore, in diagnosing dengue has been the non-specific presentation of symptoms that mirrors other AFIs. We conducted a cross-sectional serosurvey of febrile patients presenting for treatment at two facilities in the Colombian State of Norte de Santander. Our objectives for this study were (1) to establish the first prevalence estimates of DENV MSIs and DENV-Leptospira co-infections in Norte de Santander, (2) identify hematologic indices associated with clinical severity, and (3) to construct a diagnostic algorithm to provide decisional support for clinicians in under- resourced settings.

Key Findings and Contribution to Public Health Practice 1) We estimated the prevalence of DENV MSI to be 14.6% and the prevalence of leptospirosis to be 1.4% in our hospital-based convenience sample. These are the first prevalence estimates for Norte de Santander, and our findings provide evidence of their presence in the pathogen landscape. However, our findings cannot estimate the burden of disease. Enhanced surveillance and research are required, yet laboratory capacity is the critical piece to addressing these issues.

2) We found no differences in clinical severity by SSI, MSI, or among patients co-infected with Leptospira. However, we found co-infections with multiple DENV serotypes, pathogenic Leptospira spp., and with HBV. An unexpected finding of a patient with dengue, leptospirosis, and HBV highlights the utility of case-study data to detect multi-pathogen infections, which contribute to alterations in the expected clinical presentation of disease. The impact of intra-host co-infection on clinical severity, and its potential to contribute to viral emergence, deserves further consideration. Larger studies, powered to detect differences by age-sex groups, are required to further explore the clinical impact of MSIs.

3) We were unable to construct a diagnostic algorithm to assist in clinical diagnostics in areas without laboratory capacity. Our findings underscore the challenge of dengue diagnosis: clinical presentation alone is insufficient. Increasing the availability of rapid diagnostics will be essential for initiating early treatment and defining the incidence of co-infections. Further, the study of coinfection must move beyond laboratory systems and examine the outcomes of disease among persons with complex immunologic profiles. The expanding application of molecular technologies for clinical diagnosis, including the use of next-generation sequencing, will detect and define the intra-host pathogen landscape.2 The clinical interpretation of the findings, and their translation into public health practice, will be required.

Chapter III. Familial Transmission of Zaire ebolavirus

We reconstructed and fully resolved the transmission events of EBOV transmission among geographically-dispersed family members in Liberia. The objectives were to (1) describe the how single-day exposures can potentiate an epidemic, and (2) elucidate the underlying ecological determinants (i.e., human behaviors and movement) that contribute to the dynamic nature of epidemic EBOV transmission.

Key Findings and Contribution to Public Health Practice 1) We defined key epidemiologic transmission parameters using individual exposure, transmission, and clinical data. We estimated the incubation period for multi- and single-day exposures to be 6.0 and 7.0 days, respectively. Our estimates are aligned with those calculated from a meta-analysis of available data up to the West Africa epidemic,3 which determined an incubation period of 6.22 days for all exposures. With the limitations inherent in small sample sizes, our estimates provide additional measures to contribute to the pool of existing resources from which transmission parameters can be estimated with increasing precision.

2) We identified how the inability to obtain an isolation bed for the source patient resulted in health- seeking behavior and long-range travel into and out of her community during her peak infectious phase of fatal EVD. Admission to a treatment center was a critical aspect of decelerating the incidence of EVD in West Africa.4-7 Between the months of July-November 2014, in regions where focused contact tracing was combined with patient isolation, the average time from detection to isolation decreased from 6.0 days to 1.5 days,8 the number of generations from a single source decreased from four to two, the median duration of localized outbreaks decreased from 53 to 25 days, survival increased from 13% to 50%.5-6 Our results provide the granular data to explain the individual-level and impact of these interventions, and support their use for future outbreaks of EVD.

3) We described how the cases, when they proceeded to the infectious phase of EVD, isolated themselves from large numbers of family members. They were provided food and water during their self-sequestration but did not generate additional secondary or tertiary infections. This was a critical intervention employed by the family and the community, as children were kept separate from sick parents. The care of a sick child is a resource-intensive endeavor, one in which many generations of family and community members become involved. It was a sick child that was the source of an outbreak in the DRC,9 and a sick child was the index case for what are probably in excess of 30,000 cases of EVD in West Africa.10 We argue that our results demonstrate the utility and effectiveness of home- and community-based isolation for future outbreaks, but it should be an emergency measure only during an interim waiting period for creation and expansion of isolation and treatment capacity.

4) Our unexpected finding of the probable contribution of sweat to epidemic transmission highlights an under-recognized mode of transmission. The assumption has been that EBOV transmission is

limited to blood and body fluids, without consideration of sweat. Previous reports have described a high concentration of virus in and around sweat glands.11 Together, this is an important finding with implications for driving ongoing and unrecognized virus transmission.

Chapter IV. Post-Ebola Virus Disease Syndrome

Post-Ebola virus disease syndrome is a constellation of multi-system, chronic debilitating symptoms experiences by survivors of infection with ebolaviruses, and is now reported in the majority of the 17,000 survivors of the West Africa epidemic.12 Incomplete recovery from tropical virus infection may contribute to additional transmission, poor case management, and functional decline amongst vulnerable populations lacking public health infrastructure and diagnostic methodologies. We conducted physical examinations and evaluations of functional status investigate clinical symptoms and chronicle the post-EVD disability among three survivors from the transmission chain described in Chapter III. Within the limitations of ongoing EBOV transmission, our aims were to characterize as fully as possible (1) the physical and psychiatric symptoms, (2) patterns of distribution by body system, and (3) impact on functional ability.

Key Findings and Contribution to Public Health Practice 1) We used repeated physical examinations to identify symptoms and provide highly-detailed descriptions of each affected body site. These data add to the accumulating evidence from ongoing investigations in West Africa and the US, and will contribute to systematic reviews and meta- analyses for future investigations of PEVDS among affected populations.

2) In concert with other studies, we, described a symmetrical distribution of pain consistent suggestive of an immune-mediated arthritis. These findings add epidemiologic support to the hypothesis that post-viral symptoms have a pathophysiologic bases in the immune-mediated aspect of EVD.

3) We used a vetted functional scale sensitive to cultural-specific measures of disability to assess functional decline between pre-EVD baseline status and 90- and 120-day intervals after discharge from ETU. Our results describe chronic sequelae and loss of function, emphasizing the need for clinical and public health engagement to elucidate the full spectrum of PEVDS. Understanding the patterns of disability among EVD survivors will our knowledge of EVD pathophysiology and identify targets for therapies and vaccines for use in future ebolavirus outbreaks.

FUTURE DIRECTIONS

Despite decades of research into dengue and the ebolaviruses, we lack the ability to fully explain, forecast, and prevent the emergence of these viruses and those to come. The risk of human infection from animals is persistent, wide-spread, and unpredictable. Until human exposure to infection can be more accurately predicted or prevented, the approach to managing future outbreaks will center on the

classic, response approach to control: early case detection and isolation; contact tracing and community engagement; environmental assessment and mitigation; and effective, equitable clinical care. Within its limitation and criticism of lacking scientific rigor, particularly with respect to generalizability,13 case study data are informative estimates from which clinical care is coordination, research plans developed, and policy decisions are made.

A vital next step is deliverable financial support for the development and sustainment of health services in the countries at risk for emerging zoonotic viruses. The Commission on a Global Health Risk Framework for the Future has underscored the importance of resilient national public health services as the first line of defense against future epidemics of EVD or other diseases.14 They recommended investment in essential health services with an emphasis on primary prevention and treatment, at an annual cost of $4.5 billion,14 to both strengthen the ability to recognize and respond to outbreaks, with also have sustained investment for the next generation of technologies for prediction, detection, and control. No country exists in isolation; borders are too porous, and travel too rapid. Containment and isolation are inadequate public health strategies. We do not lack the science to understand the consequences of maintaining weakened nation-states. In perhaps the greatest ongoing struggle, global health security remains an inexorable adjudgment of political will.

REFERENCES

1. Sharp TM, Tomashek KM, Read JS, et al. A new look at an old disease: recent insights into the global epidemiology of dengue. Curr Epidemiol Rep. 2017;4(1):11–21.

2. Kumar N, Sharma S, Barua S, et al. Virological and immunological outcomes of coinfections. Clin Microbiol Rev. 2018;5(31):4.

3. Velásquez GE, Aibana O, Ling EJ, et al. Time from infection to disease for Ebola virus disease, a systematic review. Clin Infect Dis. 2015;61(7):1135–1140.

4. Blackley DJ, Lindblade KA, Kateh F, et al. Rapid intervention to reduce Ebola transmission in a remote village—Gbarpolu County, Liberia, 2014. MMWR Morb Mortal Wkly Rep. 2015;64(7): 175–178.

5. Kateh F, Nagbe T, Kieta A, et al. Rapid response to Ebola outbreaks in remote areas—Liberia, July–November 2014. MMWR Morb Mortal Wkly Rep. 2015;64(7):188–92.

6. Lindblade KA, Kateh F, Nagbe TK, et al. Decreased Ebola transmission after rapid response to outbreaks in remote areas, Liberia, 2014. Emerg Infect Dis. 2015;21(10):1800–1807.

7. Sharma A, Heijenberg N, Peter C, et al. Evidence for a decrease in transmission of Ebola virus— Lofa County, Liberia, June 8–November 1, 2014. MMWR Morb Mortal Wkly Rep. 2014;63:1067– 1071.

8. Nyenswah TG, Fallah M, Sieh S, et al. Controlling the last known cluster of Ebola virus disease— Liberia, January–February, 2015. MMWR Morb Mortal Wkly Rep. 2015;64(18):500–504.

9. Leroy EM, Epelboin A, Mondonge V, et al. Human Ebola outbreak resulting from direct exposure to fruit bats in Luebo, Democratic Republic of Congo, 2007. Vector Borne Zoonotic Dis. 2009;9(6):723–728.

10. Marí Saéz A, Weiss S, Nowak K, et al. Investigating the zoonotic origin of the West African Ebola epidemic. EMBO Mol Med. 2015;7(1):17–23.

11. Zaki SR, Shieh WJ, Greer PW, et al. A novel immunohistochemical assay for the detection of Ebola virus in skin: implications for diagnosis, spread, and surveillance of Ebola hemorrhagic fever. J Infect Dis. 1999;179(suppl 1):S36–S47.

12. Bausch DG. Sequelae after Ebola virus disease: even when it’s over it’s not over. Lancet Infect Dis. 2015;15(8):865–866.

13. Yin RK. Case Study Research: Design and Method. Los Angeles CA: Sage Publications Ltd.; 2009.

14. Commission on a Global Health Risk Framework for the Future; National Academy of Medicine, Secretariat. The Neglected Dimension of Global Security: A Framework to Counter Infectious Disease Crises. Washington, DC: National Academies Press. Published May 16, 2016. https://www.ncbi.nlm.nih.gov/books/NBK368394/. Accessed March 30, 2019.

APPENDIX A HISTORY OF HUMAN EBOLAVIRUS OUTBREAKS, 1976-2014

APPENDIX A PUBLISHED REPORTS OF DENGUE VIRUS MULTI-SEROTYPE INFECTIONS, BY COUNTRY AND AUTHOR

Author Diagnostic Serotypes (Frequency ) Disease Severity (Year) Methodology Bali

Megawati, D. DENV-1/2 (NS) PCR NS (2017) DENV-1/3 (NS) Belgium ex. Thailand Cnops, L. PCR DENV-1/2 (n=1) Mild disease (2014) Brazil DENV-2/3 (n=2) Andrade, EH. DENV-2/4 (n=1) PCR NS (2016) DENV-3/4 (n=1) DENV-2/3/4 (n=2)

Araújo, FM. IFA DENV-2/3 (n=1) Mild disease (2006) PCR

Colombo, TE. ELISA DENV-1/4 (n=1) Mild disease (2013) PCR

de Bruycker-Nogueira, F. Cell culture DENV-1/4 (n=1) NS (2018) PCR

dos Santos, CLS. PCR DENV-2/3 (n=1) NS (2003) Figueiredo, RM. IFA DENV-3/4 (n=4) NS (2011) PCR

Marinho, PE. DENV-2/3 (n=1) PCR No increased severity (2017) DENV-1/2/3 (n=1)

Cell culture Rocco, IM. ELISA DENV-1/2 (n=1) Mild disease (1998) PCR

Abbreviations: ELISA, enzyme-linked immunosorbent assay; ex., exported from; IFA, immunofluorescence assay; NS, not specified; PCR, polymerase chain reaction. * References are listed by author last name at conclusion of table. Table continued on page 94.

Author Diagnostic Serotypes (Frequency) Disease Severity (Year) Methodology

China ex. Sri Lanka

Meiyu, F. Cell culture DENV-2/4 (n=8) NS (1997) PCR

Peng, W. IFA DENV-2/3 (n=1) No increased severity (2005) PCR

India DENV-1/2 (n=2) Anoop, M. PCR DENV-2/3 (n=18) NS (2010) DENV-1/2/3 (n=1)

DENV-1/3 (n=4) Bharaj, P. DENV-1/4 (n=2) PCR DHF: 6/9 (67%) (2008) DENV-2/3 (n=1) DENV-3/4 (n=1)

Das, B. PCR DENV-2/3 (n=14) NS (2013)

DENV-1/3 (n=11) Khan, SA. ELISA DENV-1/4 (n=1) NS (2013) PCR DENV-2/3 (n=1)

DENV-1/2 (n=2) Mehta, TK. PCR DENV-2/3 (n=10) Mild disease; lab values WNL (2018) DENV-3/4 (n=1)

DHF: 17/28 (60.7%), including Rao, MRK. DENV-2/3 (n=74) PCR 3/3 (100%) triple-serotype (2018) DENV-1/2/3 (n=3) infections

Abbreviations: DHF, dengue hemorrhagic fever; ELISA, enzyme-linked immunosorbent assay; ex., exported from; IFA, immunofluorescence assay; NS, not specified; PCR, polymerase chain reaction; SD, severe disease; WNL, within normal limits. * References are listed by author last name at conclusion of table. Table continued on page 95.

Author Diagnostic Serotypes (Frequency) Disease Severity (Year) Methodology

India (continued) DENV-1/3 (n=9) Reddy, MN. DENV-2/3 (n=7) PCR NS (2017) DENV-1/2/3 (n=8) DENV-1/2/3/4 (n=2)

DENV-1/2 (NS) DENV-2/3 (NS) Savargaonkar, D. DENV-2/4 (NS) PCR NS (2018) DENV-3/4 (NS) DENV-2/3/4 (NS) DENV-1/2/3/4 (n=1)

Tazeen, A. PCR DENV-1/2 (n=21) SD: 3/21 (14.3%) (2017)

DENV-1/2 (n=7) SD: 12/15 (80%) Vaddadi, K. ELISA DENV-1/3 (n=1) (2017) PCR DENV-2/4 (n=5) DENV-2/4: 5/5 (100%) DENV-3/4 (n=2) classified DWWS

DENV-1/2 (n=3) Vinodkumar, CS. DENV-1/3 (n=2) PCR DHF: 11/17 (64.7%) (2013) DENV-2/3 (n=11) DENV-2/4 (n=1)

Indonesia Lardo, S. PCR DENV-2/3 (n=1) SD (2016)

Malaysia DENV-1/2 (n=34) Dhanoa, A. SD more frequent among PCR DENV-1/3 (n=5) (2016) MSI patients DENV-2/3 (n=1)

Abbreviations: DHF, dengue hemorrhagic fever; DWWS, dengue with warning signs; ELISA, enzyme-linked immunosorbent assay; ex., exported from; MSI, multi-serotype; NS, not specified; PCR, polymerase chain reaction; SD, severe disease. * References are listed by author last name at conclusion of table. Table continued on page 96.

Author Diagnostic Serotypes (Frequency) Disease Severity (Year) Methodology

Mexico DENV-1,2 (n=9) DENV-2/4 (n=2) Requena-Castro, R. ELISA DENV-1/2/3 (n=1) NS (2017) PCR DENV-1/3/4 (n=1) DENV-1/2/3/4 (n=1)

Mexico ex. Indonesia DENV-1/2 (n=2) DENV-1/3 (n=4) Loroño-Piño, MA. DENV-1/4 (n=2) PCR DHF: 3/12 (25%) (1999) DENV-2/3 (n=2) DENV-2/4 (n=1) DENV-3/4 (n=1)

Puerto Rico CF Gubler, DJ. IFA DENV-1/4 (n=1) Mild disease (1985) PRNT

New Caledonia Laille, M. PCR DENV-1/3 (n=6) NS (1991)

Peru Mamani, E. PCR DENV-1/3 (n=6) DHF: 1/6 (16.7%) (2010)

Sri Lanka Dissanayake, V. PCR DENV-1/3 (n=9) NS (2011)

Abbreviations: CF, complement fixation; DHF, dengue hemorrhagic fever; ELISA, enzyme-linked immunosorbent assay; ex., exported from; IFA, immunofluorescence assay; NS, not specified; PCR, polymerase chain reaction; PRNT, plaque reduction neutralization assay. * References are listed by author last name at conclusion of table. Table continued on page 97.

Author Diagnostic Serotypes (Frequency) Disease Severity (Year) Methodology

Thailand Sriprapun, M. PCR (urine) DENV-2/4 (n=1) DHF (2015)

Maneekarn, N. ELISA DENV-1/2 (n=2) NS (1993) PCR

DENV-1/2 (n=1) Chinnawirotpisan, P. PCR DENV-2/4 (n=1) DHF: DENV-2/4 only (2008) DENV-1/2/3 (n=1)

Pongisiri, P. PCR DENV-3/4 (n=1) NS (2012)

Taiwan

Wang, SF. PCR DENV-2/3 (n=2) NS (2016)

Vietnam

DENV-1/4 (n=17) Binh, PT. PCR DENV-2/3 (n=1) No increased severity (2009) DENV-3/4 (n=1)

Abbreviations: DHF, dengue hemorrhagic fever; ELISA, enzyme-linked immunosorbent assay; NS, not specified; PCR, polymerase chain reaction. * References are listed by author last name at conclusion of table.

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Araújo FM, Nogueira RM, de Araújo JM, et al. Concurrent infection with dengue virus type-2 and DENV-3 in a patient from Ceará, Brazil. Mem Inst Oswaldo Cruz. 2006;101(8):925-928.

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Rocco IM, Barbosa ML, Kanomata EH. Simultaneous infection with dengue 1 and 2 in a Brazilian patient. Rev Inst Med Trop Sao Paulo. 1998;40(3):151-154.

Savargaonkar D, Sinha S, Srivastava B, et al. An epidemiological study of dengue and its co-infections in Delhi. Int J Infect Dis. 2018;74:41-46.

Sriprapun M, Laosakul C, Krajiw S, et al. Time course of concurrent infection with dengue virus serotypes 2 and 4 detected in urine. Asian Biomed. 2015;9:197-202.

Tazeen A, Afreen N, Abdullah M, et al. Occurrence of co-infection with dengue viruses during 2014 in New Delhi, India. Epidemiol Infect. 2017;145(1):67-77.

Vaddadi K, Jain P, Prasad V, Venkataramana M. Co-circulation and co-infections of all dengue virus serotypes in Hyderabad, India 2014. Epidemiol Infect. 2017;145(12),2563-2574.

Vinodkumar CS, Kalapannavar NK, Basavarajappa KG, et al. Episode of co-existing infections with multiple dengue virus serotypes in central Karnataka. India J Infect Public Health. 2013;6(4):302-306.

Wang SF, Wang WH, Chang K, et al. Severe dengue fever outbreak in Taiwan. Am J Trop Med Hyg. 2016;94(1):193-197.

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APPENDIX C PATIENT-SPECIFIC FUNCTIONAL SCALE

The Patient Specific Functional Scale This useful questionnaire can be used to quantify activity limitation and measure functional outcome for patients with any orthopaedic condition. Clinician to read and fill in below: Complete at the end of the history and prior to physical examination.

Initial Assessment: I am going to ask you to identify up to three important activities that you are unable to do or are having difficulty with as a result of your problem. Today, are there any activities that you are unable to do or having difficulty with because of your problem? (Clinician: show scale to patient and have the patient rate each activity).

Follow-up Assessments: When I assessed you on (state previous assessment date), you told me that you had difficulty with (read all activities from list at a time). Today, do you still have difficulty with: (read and have patient score each item in the list)?

Patient-specific activity scoring scheme (Point to one number): 0 1 2 3 4 5 6 7 8 9 10 Unable Able to perform to activity at the perform same level as activity before injury or problem

(Date and Score)

Activity Initial 1. 2. 3. 4. 5. Additional Additional

Total score = sum of the activity scores/number of activities Minimum detectable change (90%CI) for average score = 2 points Minimum detectable change (90%CI) for single activity score = 3 points

PSFS developed by: Stratford, P., Gill, C., Westaway, M., & Binkley, J. (1995). Assessing disability and change on individual patients: a report of a patient specific measure. Physiotherapy Canada, 47, 258- 263.

Reproduced with the permission of the authors. 101

APPENDIX D LSU IRB APPROVAL—DETERMINANT FACTORS OF VIRAL DIVERSITY AND TRANSMISSION ECOLOGY OF DENGUE VIRUS IN COLOMBIA

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APPENDIX B COLOMBIA SEROPREVALENCE STUDY ADULT CONSENT AND PEDIATRIC ASSENT FORMS, ENGLISH AND SPANISH

ADULT CONSENT FORM (ENGLISH) Flesch–Kincaid Readability Scores: 6.0

Heterogeneity of Vector Exposure Alters Dengue Virus Transmission and Disease Risk Patterns

Description You had been invited to participate in a study for an investigation about dengue virus and its vector. We are planning to study the virus serotypes and the antibodies against the virus that could be circulating in your blood.

Procedures Your doctor has requested a diagnostic test to confirm an infection with dengue virus in your blood. We would like to request your permission to use the REMAINING serum in our research. Normally, this serum is discarded after the test is done. However, it will be of great use in our investigation and the results of this investigation will not change the treatment that your doctor has decided to give you. IF YOU ARE PREGNANT YOU WILL NOT BE INCLUDED IN THIS STUDY.

After completion of our main experiments, we would like to store part of your sample for future studies related to dengue. This sample will be frozen and stored with a CODE, your NAME WILL NOT BE INCLUDED on any material collected for this investigation. You can decide NOT to continue in this research at any moment and, all samples collected from you will be destroyed.

We would also like to have your permission to include the results of additional test (platelet count, red blood cell count) requested along your dengue confirmation test. We will not record any clinical procedure, treatment, past diseases or any identifiable information from your files. You may reject the access of your tests results at any time.

It is possible that in the future we can obtain new information, new products, or have any discoveries with potential commercial value. Any donor, in this case serum donor, will not be entitled to property rights on this information, product or discovery.

The results of this investigation will be obtained only for research purposes. You will not know the results of the test we will perform on your sample.

Risks and Benefits We do not anticipate any risk or secondary effect for participating in this study. Since this study will not include any treatment, you will not receive any direct benefit for your participation. We cannot guarantee you will receive any benefit from this research.

Privacy Your privacy will be protected to the extent legally possible. Information from this study may be published. We will not publish your name, address, or other information that would identify you personally or link your name with any of our results.

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Time Once the sample for the test requested by your doctor is taken, you will not be required to spend any extra time.

Payment You will not receive any payment for participating in this study.

Whom to call for questions: You can call Carolina Cardenas at 317 300 4517 any time if I have any questions about study.

Participant When I signed this form, it means that the study has been explained to me in the language that I understand. If I decide to take part of this study, I will receive a signed copy of this information and a written summary about the study. I will have the opportunity to ask questions about the study. My questions will have to be answer to my satisfaction before I signed this form. I can choose not to participate in the study, or I can quit the study at any time.

I know what will be done as part of the study. I also know the good and the bad (benefits and risks) that could happen if I take part in the study. I know that I can stop been part of the study and I will keep getting the usual medical attention.

I consent that my sample ____or other tests results ____will be used in this research.

I do NOT consent that my sample ____ or other test results ____will be used in this research.

I consent that my sample will be stored for future investigations. ____

Printed Name of Participant Date of Birth

Street Address City

Printed Name of Person Conducting Consent Signature of Person Conducting Consent Interview Interview

Printed Name of Witness Witness Signature Date

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CONSENTIMIENTO INFORMADO PARA ADULTOS (SPANISH)

Descripción: Usted está invitado para participar en un estudio de investigación sobre el dengue y su vector. Nosotros planeamos estudiar los serotipos de dengue y los anticuerpos en contra del virus y de la saliva del vector encontradas en su sangre.

Procedimientos: A usted se le ha solicitado un examen de sangre para confirmar la infección por el virus del dengue. Nos gustaría que nos diera su permiso para estudiar el suero RESTANTE O QUE SOBRE de la realización de dicho examen. Ese suero sobrante normalmente es eliminado. Este estudio de investigación no cambiará su tratamiento médico. SI ESTAS EMBARASADA NO SERAS INCLUIDA EN ESTE ESTUDIO.

Después de completar nuestros exámenes iniciales, quisiéramos guardar el suero sobrante para hacer futuras investigaciones sobre el dengue. Ese suero sería congelado y almacenado con un número o CÓDIGO y NO SE INCLUIRA SU NOMBRE. En cualquier momento del estudio usted puede retirarse, y en ese caso su suero sería desechado.

Al mismo tiempo, quisiéramos su permiso para acceder a los resultados de otros exámenes que se pidieron junto con en examen de confirmación del dengue, con el UNICO propósito de obtener datos de hemograma (componentes de su sangre como plaquetas y glóbulos rojos). Es importante aclarar que no se tomara ninguna información personal como su nombre o dirección o cualquier otra información de enfermedades pasadas. Usted puede rechazar en cualquier momento la inclusión de estos exámenes.

Con el uso de su suero puede suceder que en el futuro se obtengan nuevos productos, exámenes o descubrimientos los cuales tienen potencial valor comercial. Los donantes de tejidos, en este caso el suero extraído de su sangre, no retienen ningún derecho de propiedad sobre esos productos. Por lo tanto, usted no recibiría ningún beneficio económico de esos productos, exámenes o descubrimientos. Los resultados del estudio realizado con su muestra serán usados únicamente con propósitos de investigación y usted no conocerá el resultado de dichas pruebas.

Riesgos y Beneficios No se prevén riesgos asociados con este estudio. Como este no es un estudio sobre tratamiento, usted no recibirá ningún beneficio directo por su participación. Nosotros no podemos garantizarle que usted recibirá algún beneficio de este estudio.

Privacidad Su privacidad será protegida al máximo posible. La información resultante de este estudio podría ser publicada. Nosotros no publicaremos su nombre, dirección o cualquier otra información que lo pueda ligar o identificar a usted con los resultados de esta investigación.

Gasto de Tiempo Una vez se obtenga la muestra de sangre para realizar sus exámenes médicos, su participación en este estudio no requerirá de tiempo adicional para usted.

Pagos Usted no recibirá pago alguno por participar en este estudio.

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A quien llamar por preguntas: Usted puede llamar a Carolina Cárdenas al 317 3004517 a cualquier hora si tiene alguna pregunta sobre el estudio.

Paciente Cuando yo firmo este formato, significa que el estudio de investigación me ha sido explicado en un lenguaje que yo entiendo. Si yo decido participar, yo recibiré una copia de esta información y de un resumen del estudio. Yo tendré la oportunidad de hacer preguntas sobre el estudio. Mis preguntas deberán ser respondidas a satisfacción antes que yo firme este formato. Yo puedo decidir no participar en el estudio o puedo renunciar a él en cualquier momento.

Yo sé que se evaluará en este estudio. Yo también se lo bueno y lo malo (riesgos y beneficios) que podría pasar si yo hago parte de este estudio. Yo sé que puedo dejar de ser parte de esta investigación sin dejar de recibir mi atención médica usual. Marque los que considere pertinentes:

Yo consiento en que mi muestra____o exámenes clínicos ___sean usados para esta investigación.

Yo NO consiento en que mi muestra___o exámenes clínicos___sea usados para esta investigación.

Yo consiento que mi muestra sea guardada para otras investigaciones: SI_____ NO ______

Nombre del Paciente Fecha de Nacimiento

Firma del Paciente Fecha

Barrio Ciudad de Residencia

Nombre del Testigo Firma del testigo Fecha

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PEDIATRIC ASSENT FORM Flesch–Kincaid Readability Scores: 6.0

Heterogeneity of Vector Exposure Alters Dengue Virus Transmission and Disease Risk Patterns

Description of the Study Mosquitoes carry dengue virus. The purpose of this study is to look for virus and other molecules in the blood that are made when a person becomes sick. These molecules, called antibodies, are made by the body to protect against being sick.

Your child is being asked to participate in a study about dengue virus. Mosquitoes in your area carry this virus. When they bite people, this can cause a person to become sick with dengue. The data from this study will help to better control the mosquito population. It will also help us to understand how and when the body makes the molecules to protect against getting sick.

This study will test your child’s blood for virus to see if he or she has the virus. We will also see if he or she has the molecules to protect against getting sick. Study staff will take blood two times. The first time will be today, if you want your child to take part in the study. The second time will be in six months.

Possible Benefits There is no benefit to you or your child for being in this study. The benefit of being in this study is the benefit of the community. It will also help guide mosquito control efforts and help people who get sick with dengue.

Possible Risks The risk of side effects from drawing blood is small. Your child may have pain when the needle goes into the skin. Some people have a little bruising after the blood is taken.

Costs There is no cost to be in this study.

Privacy Your child’s privacy will be protected to the extent legally possible. Information from this study may be published. We will not publish your name or your child’s name, address, or other information that would identify you or your child or link you or your child to antibody results.

Contacting Study Personnel You can call the study staff if you have questions about the study. You can also contact the study staff if you want your child to stop being part of the study. The contact information is: (Insert site-specific investigator contact information.)

Consent This study staff has been explained to me, and I freely consent to join this study. I understand the following:

• There are small risks from getting a blood draw. • There are no direct benefits to me from being in this study. • I may refuse to join or may drop out at any time.

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Printed Name of Participant Date of Birth

Signature of Participant or Consenting Parent Date/Time

Street Address Barrio, City

Printed Name of Person Conducting Consent Signature of Person Conducting Consent Date Interview Interview

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CONSENTIMIENTO INFORMADO PEDIATRICO (SPANISH)

Factores Determinantes de Diversidad Viral y Ecología de la Transmisión del Virus del Dengue en Colombia Descripción Usted está invitado para participar en un estudio de investigación sobre el dengue y su vector. Nosotros planeamos estudiar los serotipos de dengue y los anticuerpos en contra del virus y de la saliva del vector encontradas en su sangre.

Procedimientos A usted se le ha solicitado un examen de sangre para confirmar la infección por el virus del dengue. Nos gustaría que nos diera su permiso para estudiar el suero RESTANTE O QUE SOBRE de la realización de dicho examen. Ese suero sobrante normalmente es eliminado. Este estudio de investigación no cambiará su tratamiento médico. SI ESTAS EMBARASADA NO SERAS INCLUIDA EN ESTE ESTUDIO.

Gasto de Tiempo Una vez se obtenga la muestra de sangre para realizar sus exámenes médicos, su participación en este estudio no requerirá de tiempo adicional para usted.

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Pagos Usted no recibirá pago alguno por participar en este estudio.

A quien llamar por preguntas: Usted puede llamar a Carolina Cárdenas al 317 3004517 a cualquier hora si tiene alguna pregunta sobre el estudio.

Marque los que considere pertinentes:

Yo consiento en que mi muestra ______o exámenes clínicos ______sean usados para esta investigación.

Yo NO consiento en que mi muestra ______o exámenes clínicos_____ sea usados para esta investigación.

Yo consiento que mi muestra sea guardada para otras investigaciones: SI_____ No ______

Nombre del Paciente Fecha de Nacimiento

Nombre del Acudiente

Firma del Paciente o Acudiente Fecha

Barrio Ciudad de Residencia

Nombre del Testigo Firma del testigo Fecha

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APPENDIX C CENTERS FOR DISEASE CONTROL AND PREVENTION DENGUE CASE REPORT FORM

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APPENDIX G ADULT AND PEDIATRIC CLINICAL LABORATORY REFERENCE RANGES

Adult American Board of Internal Medicine Clinical Laboratory Reference Values* (Eryth) Erythrocytes 4.2–5.9 ×106/ L (Hct) Hematocrit female: 37%–47% male: 42%–50%μ (Hb) Hemoglobin female: 12–16 g/dL male: 14–18 g/dL (Plt) Platelets 150,000–450,000/ L (Leu) Leukocytes 4.0–11.0 ×103/ L (Neut) Neutrophils 50%–70% μ (Lymph) Lymphocytes 30%–45% μ (Mono) Monocytes 0–6% (Eos) Eosinophils 0–3% (Baso) Basophils 0–1%

Pediatric Clinical Laboratory Reference Values† (Eryth) Erythrocytes Age ×103/ L 0–30 days 4.1–6.7 μ 1 month 3.0–5.4 2–6 months 2.7–4.5 7 months–2 years 3.7–5.3 3–6 year 3.9–5.3 7–12 years 4.0–5.2 13–18 years female 4.1–5.1 male 4.5–5.3 ≥19 years female 4.2–5.4 male 4.7–6.0

(Hct) Hematocrit Age % 0–30 days 44–70 1 month 32–42 2–6 months 29–41 7 months–2 years 33–39 3–6 years 34–40 7–12 years 35–45 13–18 years female 36–45 male 37–49 ≥19 years female 36–46 male 41–53

(Hb) Hemoglobin Age g/dL 0–30 days 15.0–22.0 1 month 10.5–14.0

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2–6 months 9.5–13.5 7 months–2 years 10.5–14.0 3–6 years 11.5–14.5 7–12 years 11.5–15.5 13–18 years female 12.0–16.0 male 13.0–16.0 ≥19 years female 12.0–16.0 male 13.5–17.5

(Plt) Platelets Age, sex inclusive 150,000–450,000/ L

μ (Leu) Leukocytes Age ×103/ L 0–30 days 9.1–34.0 μ 1 month 5.0–19.5 2–11 months 6.0–17.5 1–6 years 5.0–14.5 7–12 years 5.0–14.5 13–18 years 4.5–13.5 ≥19 years 4.5–11.0

Neutrophils Lymphocytes Monocytes Eosinophils Basophils Age (Neut) (%) (Lymph) (%) (Mono) (%) (Eos) (%) (Baso) (%) 0–30 days 32–67 25–37 0–9 0–2 0–1 1 month 20–46 28–84 0–7 0–3 0–1 2–11 months 20–48 34–88 0–5 0–3 0–1 1–6 years 37–71 17–67 0–5 0–3 0–1 7–12 years 33–76 15–61 0–5 0–3 0–1 13–18 years 33–76 15–55 0–4 0–3 0–1

*https://www.abim.org/~/media/ABIM%20Public/Files/pdf/exam/laboratory-reference-ranges.pdf †From: Andropoulos DB. Pediatric Normal Laboratory Values. In Gregory's Pediatric Anesthesia (eds G. A. Gregory and D. B. Andropoulos). 2011.

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APPENDIX D COLOMBIA SEROPREVALENCE SURVEY SAS CODE

****************************************** 0 = 'negative' proc printto print=myprint; 1 = 'positive'; proc printto log=mylog; value multipos proc format; 0 = 'mono inf' value agecat 1 = 'multi inf'; 1 = '<1' value yesno 2 = '1-9' 0 = 'no' 3 = '10-17' 1 = 'yes'; 4 = '18-29' value sex 5 = '30-39' 0 = 'female' 6 = '40-49' 1 = 'male'; 7 = '50-64' value denvpos 8 = '65+'; -1 = 'missing' value agetwo 0 = 'negative' 0 = '<18' 1 = 'positive'; 1 = '18+'; value cities value serodenv 1 = 'OCAÑA' 0 = 'DENV neg' 2 = 'PATIOS' 1 = 'DENV1 pos' 3 = 'OTHER'; 2 = 'DENV2 pos' value response 3 = 'DENV3 pos' 1 = 'missing data' 4 = 'DENV4 pos' 2 = 'data available'; 5 = 'DENV1,DENV2 pos' value singcomb 6 = 'DENV1,DENV3 pos' 0 = 'denv neg' 7 = 'DENV1,DENV4 pos' 1 = 'denv single positive' 8 = 'DENV2,DENV3 pos' 2 = 'denv multi positive'; 9 = 'DENV2,DENV4 pos' value sympdays 10 = 'DENV3,DENV4 pos' 0 = '<4 days ill' 11 = 'DENV1,DENV2,DENV3 pos' 1 = '4+ days'; 12 = 'DENV1,DENV3,DENV4 pos' value serology 13 = 'DENV2,DENV3,DENV4 pos'; 0 = 'missing' value combdenv 1 = 'IgM pos' 0 = 'DENV neg' 2 = 'IgG pos' 1 = 'DENV1 pos, only' 3 = 'IgM & IgG pos'; 2 = 'DENV2 pos, only' data disser; 3 = 'DENV3 pos, only' set new.new_jen_data; 4 = 'DENV4 pos, only' if denvpos = . then delete; 5 = 'combined DENV'; *proc contents position data=disser; value pos *run;

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*endsas; if denv2 = 1 and denv3 = 1 then ************************************ serodenv=8; * change character to numeric if denv2 = 1 and denv4 = 1 then * reset . to blank serodenv=9; ************************************; if denv3 = 1 and denv4 = 1 then if DAYSSX = '.' then DAYSSX = ' '; serodenv=10; DAYSSX_n = DAYSSX*1; if denv1 = 1 and denv2 = 1 and denv3 = 1 ******************************* then serodenv=11; * agecat variable if denv1 = 1 and denv3 = 1 and denv4 = 1 *******************************; then serodenv=12; if age lt 1 then agecat = 1; if denv2 = 1 and denv3 = 1 and denv4 = 1 if age ge 1 and age lt 10 then agecat = 2; then serodenv=12; if age ge 10 and age lt 18 then agecat = 3; ********************************* if age ge 18 and age lt 30 then agecat = 4; * combdenv if age ge 30 and age lt 40 then agecat = 5; *********************************; if age ge 40 and age lt 50 then agecat = 6; combdenv = 0; if age ge 50 and age lt 65 then agecat = 7; if denvpos = . then combdenv=.; if age ge 65 then agecat=8; if denv1 = 1 then combdenv=1; **************************************** if denv2 = 1 or denv2 = 2 then * dichotomous age combdenv=2; ****************************************; if denv3 = 1 then combdenv=3; if age lt 18 then agetwo = 0; if denv4 = 1 then combdenv=4; if age ge 18 then agetwo = 1; if serodenv ge 5 then combdenv=5; *********************************** *************************************** * fix error in denv2 * neg, single pos, comb pos ***********************************; ***************************************; if denv2 = 2 then denv2 = 1; if combdenv = 0 then sing_comb = 0; ********************************** if combdenv ge 1 and combdenv le 4 then * serotype DENV sing_comb = 1; **********************************; if combdenv eq 5 then sing_comb = 2; serodenv = 0; ************************************** if denvpos = . then serodenv = .; * sex variable if denv1 = 1 then serodenv=1; **************************************; if denv2 = 1 then serodenv=2; if sex = 'F' then sex = 0; if denv3 = 1 then serodenv=3; if sex = 'M' then sex = 1; if denv4 = 1 then serodenv=4; ************************************* if denv1 = 1 and (denv2 = 1 or denv2 = 2) * fix denvpos and denv then serodenv=5; *************************************; if denv1 = 1 and denv3 = 1 then if denvpos = . then denvpos = -1; serodenv=6; if denv1 = . then denv1 = -1; if denv1 = 1 and denv4 = 1 then if denv2 = . then denv2 = -1; serodenv=7; if denv3 = . then denv3 = -1;

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if denv4 = . then denv4 = -1; set disser; ************************************* if denvpos = -1 then delete; * cities variable data no_miss_all; *************************************; set no_miss_denv; if (city = 'ABREGO' or city = 'BOGOTA' or if admit = . then delete; city = 'CHICANOTA' or run; city = 'COCHIRA' or city = data single; 'CONVENCION' or city = 'CUCUTA' or set disser; city = 'EL CARMEN' or city = if combdenv = 5 then delete; 'GONZALES' or city = 'GUAMALITO' or data positive; city = 'HACARI' or city = 'LA set disser; ESPERANZA' or city = 'LA PLAYA' or if denvpos = 0 then delete; city = 'RIO CESAR' or city = 'SAN ************************************** PABLO' or city = 'TARRA' or * single or multiple pos city = 'TEORAMA') then cities = 3; **************************************; if city = 'OCAÑA' then cities = 1; multi_pos = 0; if city = 'PATIOS' then cities = 2; if serodenv ge 5 then multi_pos = 1; ************************************** ************************************** * response rate information * symptom days **************************************; **************************************; if denvpos = -1 then sero_response = 1; symp_days = 0; if denvpos ge 0 then sero_response = 2; if dayssx ge 4 then symp_days=1; if admit = . then clin_response = 1; data complete; if admit ge 0 then clin_response = 2; set positive; if rbc = . then blood_response = 1; if vomit = . then delete; if rbc ge 0 then blood_response = 2; run; ************************************* /* * fix gingbleed proc contents position data=disser; *************************************; */ if gingbleed = -1 then gingbleed = .; proc freq data=disser; **************************************** tables serodenv; * serology tables agecat; ****************************************; tables sex; serology = 0; tables lepto; if igm = 1 then serology = 1; tables serodenv; if igg = 1 then serology = 2; tables combdenv; if igm = 1 and igg = 1 then serology = 3; tables serology; ************************************* tables agecat*denvpos; * admit tables sex*denvpos; **************************************; tables city; if admit = 2 then admit = 1; tables city*denvpos; data no_miss_denv; tables serology*denvpos;

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tables city*serodenv; run; tables lepto*denvpos; /* tables serodenv*denvpos; proc logistic data=positive; tables combdenv*denvpos; model multi_pos = vomit; format serodenv serodenv. agecat agecat. format multi_pos multipos. vomit yesno.; denvpos denvpos. sex sex. run; serology serology. combdenv */ combdenv.; proc sort data=complete; run; by symp_days; proc means data=disser; run; var age; proc freq data=complete; run; tables symp_days*multi_pos/all; proc sort data=disser; format symp_days sympdays. multi_pos by denvpos; multipos.; run; run; proc means data=disser; proc sort data=complete; class denvpos; by myalgia; var age; run; format denvpos denvpos.; proc freq data=complete; run; tables myalgia*multi_pos/all; proc freq data=complete; format multi_pos multipos.; tables multi_pos; run; run; proc sort data=complete; proc sort data=complete; by retropain; by sex; run; run; proc freq data=complete; proc freq data=complete; tables retropain*multi_pos/all; tables sex*multi_pos/all fisher; format multi_pos multipos.; format sex sex. multi_pos multipos.; run; run; proc sort data=complete; /* by jntpain; proc logistic data=positive; run; model multi_pos = sex; proc freq data=complete; format multi_pos multipos. sex sex.; tables jntpain*multi_pos/all; run; format multi_pos multipos.; */ run; proc sort data=complete; proc sort data=complete; by vomit; by rash; run; run; proc freq data=complete; proc freq data=complete; tables vomit*multi_pos/all; tables rash*multi_pos/all; format multi_pos multipos. vomit yesno.; format multi_pos multipos.;

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run; run; proc sort data=complete; proc sort data=complete; by abdpain; by gingbleed; run; run; proc freq data=complete; proc freq data=complete; tables abdpain*multi_pos/all; tables gingbleed*multi_pos/all; format multi_pos multipos.; format multi_pos multipos.; run; run; proc sort data=complete; proc sort data=complete; by ha; by agetwo; run; run; proc freq data=complete; proc freq data=complete; tables ha*multi_pos/all; tables agetwo*multi_pos/all; format multi_pos multipos.; format multi_pos multipos. agetwo agetwo.; run; run; proc sort data=complete; proc sort data=complete; by larvae; by icterus; run; run; proc freq data=complete; proc freq data=complete; tables larvae*multi_pos/all; tables icterus*multi_pos/all; format multi_pos multipos.; format multi_pos multipos.; run; run; proc sort data=complete; proc sort data=complete; by diarrhea; by hyperemia; run; run; proc freq data=complete; proc freq data=complete; tables diarrhea*multi_pos/all; tables hyperemia*multi_pos/all; format multi_pos multipos.; format multi_pos multipos.; run; run; proc sort data=complete; proc sort data=complete; by diarrhea; by OLIGUR; run; proc freq data=complete; proc freq data=complete; tables oligur*multi_pos/all; tables diarrhea*multi_pos/all; format multi_pos multipos.; format multi_pos multipos.; run; run; proc sort data=complete; proc sort data=complete; by petech; by fever; proc freq data=complete; run; tables petech*multi_pos/all; proc freq data=complete; format multi_pos multipos.; tables fever*multi_pos/all; run; format multi_pos multipos.; proc sort data=complete;

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by nosebleed; ******************************************** proc freq data=complete; * frequencies with complete data tables nosebleed*multi_pos/all; ********************************************; format multi_pos multipos.; ******************************************** run; ** proc sort data=complete; * all data including missing by hypotn; ******************************************** proc freq data=complete; **; tables hypotn*multi_pos/all; proc means data=disser; format multi_pos multipos.; var age DENVPOS DENV1 DENV2 run; DENV3 DENV4 IGM IGG LEPTO rbc proc sort data=complete; PLT by tachy; HB HCT WBC NEUT EOS LYMPH proc freq data=complete; MONO BASO DAYSSX_n; tables tachy*multi_pos/all; title 'all data including missing'; format multi_pos multipos.; proc means data=no_miss_all; run; var age DENVPOS DENV1 DENV2 proc sort data=complete; DENV3 DENV4 IGM IGG LEPTO PLT by bednet; HB HCT WBC NEUT EOS LYMPH proc freq data=complete; MONO DAYSSX_n; tables bednet*multi_pos/all; title 'complete data'; format multi_pos multipos.; proc means data=no_miss_denv; run; var rbc baso plt; proc sort data=complete; title 'data for rbc, baso, plt - missing by admit; denvpos taken out only'; proc freq data=complete; proc freq data=no_miss_all; tables admit*multi_pos/all; tables sex city cities dayssx_n ADMIT format multi_pos multipos.; DENVCLASS FEVER MYALGIA run; GINGBLEED endsas; VOMIt ICTERUS RETROPAIN ******************************************* JNTPAIN HYPEREMIA RASH OLIGUR * response information PETECH DIARRHEA NOSEBLEED *******************************************; ABDPAIN HA proc freq data=disser; HYPOTN TACHY BEDNET tables sero_response clin_response LARVAE LARVLOC DENVPOS DENV1 blood_response; DENV2 DENV3 DENV4 agecat; tables sero_response*clin_response; format agecat agecat. denvpos denv1 denv2 tables sero_response*blood_response; denv3 denv4 denvpos. cities cities.; tables blood_response*clin_response; title 'complete data'; format sero_response clin_response run; blood_response response.; /* run; proc means data=disser;

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var age DENVPOS DENV1 DENV2 proc ttest data=no_miss_denv; DENV3 DENV4 IGM IGG LEPTO RBC class denvpos; PLT var rbc plt baso; HB HCT WBC NEUT EOS LYMPH format denvpos pos.; MONO BASO DAYSSX_n; title 'data for rbc, baso, and plt - missing title 'data including missing denvpos'; denvpos taken out'; proc freq data=disser; proc freq data=no_miss_all; tables sex city dayssx_n ADMIT tables (cities FEVER MYALGIA DENVCLASS FEVER MYALGIA GINGBLEED GINGBLEED VOMIt ICTERUS RETROPAIN VOMIt ICTERUS RETROPAIN JNTPAIN HYPEREMIA RASH OLIGUR JNTPAIN HYPEREMIA RASH OLIGUR PETECH DIARRHEA NOSEBLEED PETECH DIARRHEA NOSEBLEED ABDPAIN HA ABDPAIN HA HYPOTN TACHY BEDNET HYPOTN TACHY BEDNET LARVAE)*denvpos/chisq; LARVAE LARVLOC DENVPOS DENV1 format denvpos pos. cities cities.; DENV2 DENV3 DENV4 agecat cities; title 'complete data'; format agecat agecat. denvpos denv1 denv2 run; denv3 denv4 denvpos. cities cities.; proc sort data=no_miss_all; run; by sex; */ run; *************************************** proc freq data=no_miss_all; * frequencies according to denvpos tables sex*denvpos/all; including combined denv format sex sex. denvpos pos.; ***************************************; run; proc sort data=no_miss_all; proc logistic data=no_miss_all; by denvpos; model denvpos = sex; run; format denvpos pos. sex sex.; proc freq data=no_miss_all; run; tables (sex cities)*denvpos/chisq; proc sort data=no_miss_all; format denvpos pos. cities cities.; by vomit; title 'complete data'; run; run; proc freq data=no_miss_all; proc ttest data=no_miss_all; tables vomit*denvpos/all; class denvpos; format denvpos pos. vomit yesno.; var age HB HCT WBC NEUT EOS run; LYMPH MONO; proc logistic data=no_miss_all; format denvpos pos.; model denvpos = vomit; title 'complete data'; format denvpos pos. vomit yesno.; run; run; proc sort data=no_miss_denv; ******************************************** by denvpos; single, multiple denv positive

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********************************************; format sing_comb singcomb.; proc sort data=no_miss_all; title 'Unadjusted ANOVA (sing_comb), by sing_comb; mean wbc'; run; run; proc freq data=no_miss_all; proc anova data=no_miss_all; tables (sex cities)*sing_comb/chisq; class sing_comb; format sing_comb singcomb. cities cities.; model neut=sing_comb; title 'complete data - single and multiple means sing_comb; DENV pos'; means sing_comb/lsd; run; format sing_comb singcomb.; proc anova data=no_miss_all; title 'Unadjusted ANOVA (sing_comb), class sing_comb; mean neut'; model age=sing_comb; run; means sing_comb; proc anova data=no_miss_all; means sing_comb/lsd; class sing_comb; format sing_comb singcomb.; model eos=sing_comb; title 'Unadjusted ANOVA (sing_comb), means sing_comb; mean age'; means sing_comb/lsd; run; format sing_comb singcomb.; proc anova data=no_miss_all; title 'Unadjusted ANOVA (sing_comb), class sing_comb; mean eos'; model hb=sing_comb; run; means sing_comb; proc anova data=no_miss_all; means sing_comb/lsd; class sing_comb; format sing_comb singcomb.; model lymph=sing_comb; title 'Unadjusted ANOVA (sing_comb), means sing_comb; mean hb'; means sing_comb/lsd; run; format sing_comb singcomb.; proc anova data=no_miss_all; title 'Unadjusted ANOVA (sing_comb), class sing_comb; mean lymph'; model hct=sing_comb; run; means sing_comb; proc anova data=no_miss_all; means sing_comb/lsd; class sing_comb; format sing_comb singcomb.; model mono=sing_comb; title 'Unadjusted ANOVA (sing_comb), means sing_comb; mean hct'; means sing_comb/lsd; run; format sing_comb singcomb.; proc anova data=no_miss_all; title 'Unadjusted ANOVA (sing_comb), class sing_comb; mean mono'; model wbc=sing_comb; run; means sing_comb; proc sort data=no_miss_denv; means sing_comb/lsd; by sing_comb;

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run; run; proc anova data=no_miss_denv; proc ttest data=single; class sing_comb; class denvpos; model rbc=sing_comb; var age RBC PLT HB HCT WBC NEUT means sing_comb; EOS LYMPH MONO BASO; means sing_comb/lsd; format denvpos pos.; format sing_comb singcomb.; title 'ttest by covariates according to title 'Unadjusted ANOVA (sing_comb), singular denvpos'; mean rbc'; run; run; proc freq data=single; proc anova data=no_miss_denv; tables (FEVER MYALGIA GINGBLEED class sing_comb; VOMIt ICTERUS RETROPAIN model plt=sing_comb; JNTPAIN HYPEREMIA RASH OLIGUR means sing_comb; PETECH DIARRHEA NOSEBLEED means sing_comb/lsd; ABDPAIN HA format sing_comb singcomb.; HYPOTN TACHY BEDNET title 'Unadjusted ANOVA (sing_comb), LARVAE)*denvpos/chisq; mean platelet'; format denvpos pos. FEVER MYALGIA run; GINGBLEED proc anova data=no_miss_denv; VOMIt ICTERUS RETROPAIN class sing_comb; JNTPAIN HYPEREMIA RASH OLIGUR model baso=sing_comb; PETECH DIARRHEA NOSEBLEED means sing_comb; ABDPAIN HA means sing_comb/lsd; HYPOTN TACHY BEDNET format sing_comb singcomb.; LARVAE yesno.; title 'Unadjusted ANOVA (sing_comb), title 'frequencies according to singular mean baso'; denvpos'; run; run; endsas; endsas; /* */ *************************************** /* * frequencies according to denvpos only *************************************** singular * frequencies according to combdenv ***************************************; singular individual denv1-4 proc sort data=single; ***************************************; by denvpos; proc sort data=single; run; by combdenv; proc freq data=single; run; tables (sex city hood)*denvpos/chisq; proc freq data=single; format denvpos pos.; tables (sex city hood)*combdenv/chisq; title 'frequencies according to singular format combdenv combdenv.; denvpos';

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title 'frequencies according to singular means combdenv; denvpos'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=single; title 'Unadjusted ANOVA (combdenv), class combdenv; mean hct'; model age=combdenv; run; means combdenv; proc anova data=single; means combdenv/lsd; class combdenv; format combdenv combdenv.; model wbc=combdenv; title 'Unadjusted ANOVA (combdenv), means combdenv; mean age'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=single; title 'Unadjusted ANOVA (combdenv), class combdenv; mean wbc'; model rbc=combdenv; run; means combdenv; proc anova data=single; means combdenv/lsd; class combdenv; format combdenv combdenv.; model neut=combdenv; title 'Unadjusted ANOVA (combdenv), means combdenv; mean rbc'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=single; title 'Unadjusted ANOVA (combdenv), class combdenv; mean neut'; model plt=combdenv; run; means combdenv; proc anova data=single; means combdenv/lsd; class combdenv; format combdenv combdenv.; model eos=combdenv; title 'Unadjusted ANOVA (combdenv), means combdenv; mean plt'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=single; title 'Unadjusted ANOVA (combdenv), class combdenv; mean eos'; model hb=combdenv; run; means combdenv; proc anova data=single; means combdenv/lsd; class combdenv; format combdenv combdenv.; model lymph=combdenv; title 'Unadjusted ANOVA (combdenv), means combdenv; mean hb'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=single; title 'Unadjusted ANOVA (combdenv), class combdenv; mean lymph'; model hct=combdenv; run;

123 proc anova data=single; ***************************************; class combdenv; proc sort data=positive; model mono=combdenv; by combdenv; means combdenv; run; means combdenv/lsd; proc freq data=positive; format combdenv combdenv.; tables (sex city hood)*combdenv/chisq; title 'Unadjusted ANOVA (combdenv), format combdenv combdenv.; mean mono'; title 'frequencies according to singular run; denvpos, positives only'; proc anova data=single; run; class combdenv; proc anova data=positive; model baso=combdenv; class combdenv; means combdenv; model age=combdenv; means combdenv/lsd; means combdenv; format combdenv combdenv.; means combdenv/lsd; title 'Unadjusted ANOVA (combdenv), format combdenv combdenv.; mean baso'; title 'Unadjusted ANOVA (combdenv), run; mean age, positives only'; proc freq data=single; run; tables (FEVER MYALGIA GINGBLEED proc anova data=positive; VOMIt ICTERUS RETROPAIN class combdenv; JNTPAIN HYPEREMIA RASH OLIGUR model rbc=combdenv; PETECH DIARRHEA NOSEBLEED means combdenv; ABDPAIN HA means combdenv/lsd; HYPOTN TACHY BEDNET format combdenv combdenv.; LARVAE)*combdenv/chisq; title 'Unadjusted ANOVA (combdenv), format combdenv combdenv. FEVER mean rbc, positives only'; MYALGIA GINGBLEED run; VOMIt ICTERUS RETROPAIN proc anova data=positive; JNTPAIN HYPEREMIA RASH OLIGUR class combdenv; PETECH DIARRHEA NOSEBLEED model plt=combdenv; ABDPAIN HA means combdenv; HYPOTN TACHY BEDNET means combdenv/lsd; LARVAE yesno.; format combdenv combdenv.; title 'frequencies according to singular title 'Unadjusted ANOVA (combdenv), denvpos'; mean plt, positives only'; run; run; endsas; proc anova data=positive; */ class combdenv; *************************************** model hb=combdenv; * frequencies according to individual denv1- means combdenv; 4 among positives only means combdenv/lsd;

124 format combdenv combdenv.; model lymph=combdenv; title 'Unadjusted ANOVA (combdenv), means combdenv; mean hb, positives only'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=positive; title 'Unadjusted ANOVA (combdenv), class combdenv; mean lymph, positives only'; model hct=combdenv; run; means combdenv; proc anova data=positive; means combdenv/lsd; class combdenv; format combdenv combdenv.; model mono=combdenv; title 'Unadjusted ANOVA (combdenv), means combdenv; mean hct, positives only'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=positive; title 'Unadjusted ANOVA (combdenv), class combdenv; mean mono, positives only'; model wbc=combdenv; run; means combdenv; proc anova data=positive; means combdenv/lsd; class combdenv; format combdenv combdenv.; model baso=combdenv; title 'Unadjusted ANOVA (combdenv), means combdenv; mean wbc, positives only'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=positive; title 'Unadjusted ANOVA (combdenv), class combdenv; mean baso, positives only'; model neut=combdenv; run; means combdenv; proc freq data=positive; means combdenv/lsd; tables (FEVER MYALGIA GINGBLEED format combdenv combdenv.; VOMIt ICTERUS RETROPAIN title 'Unadjusted ANOVA (combdenv), JNTPAIN HYPEREMIA RASH OLIGUR mean neut, positives only'; PETECH DIARRHEA NOSEBLEED run; ABDPAIN HA proc anova data=positive; HYPOTN TACHY BEDNET class combdenv; LARVAE)*combdenv/chisq; model eos=combdenv; format combdenv combdenv. FEVER means combdenv; MYALGIA GINGBLEED means combdenv/lsd; VOMIt ICTERUS RETROPAIN format combdenv combdenv.; JNTPAIN HYPEREMIA RASH OLIGUR title 'Unadjusted ANOVA (combdenv), PETECH DIARRHEA NOSEBLEED mean eos, positives only'; ABDPAIN HA HYPOTN TACHY run; BEDNET LARVAE yesno.; proc anova data=positive; title 'frequencies according to singular class combdenv; denvpos, positives only';

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run; tables combdenv*sex; endsas; format combdenv combdenv.; proc freq data=disser; /* tables agecat denvpos denv1 denv2 denv3 proc univariate freq data=disser; denv4 serodenv combdenv; var age; format agecat agecat. serodenv serodenv. */ combdenv combdenv.; run; run; endsas; proc freq data=disser; ******************************************; tables city*denvpos; options ls=80 ps=60 nocenter; tables sex*denvpos; libname new 'c:\jennifer\data'; run; options FORMCHAR="|----|+|---+=|-/\<>*"; proc sort data=disser; filename myprint 'c:\jennifer\prog\jen6.lst'; by denvpos; filename mylog 'c:\jennifer\prog\jen6.log'; run; proc printto print=myprint; proc means; proc printto log=mylog; class denvpos; proc format; var age rbc plt hb hct wbc neut eos lymph value agecat mono baso; 1 = '<1' run; 2 = '1-9' proc freq data=disser; 3 = '10-17' tables denvpos*(serodenv combdenv); 4 = '18-29' format serodenv serodenv. combdenv 5 = '30-39' combdenv.; 6 = '40-49' run; 7 = '50-64' /* 8 = '65+'; proc freq data=disser; value agetwo tables denv2*(denv1 denv3 denv4 0 = '<18' serodenv); 1 = '18+'; format serodenv serodenv.; value serodenv run; 0 = 'DENV neg' */ 1 = 'DENV1 pos' proc sort data=disser; 2 = 'DENV2 pos' by combdenv; 3 = 'DENV3 pos' run; 4 = 'DENV4 pos' proc means data=disser; 5 = 'DENV1,DENV2 pos' class combdenv; 6 = 'DENV1,DENV3 pos' var age rbc plt hb hct wbc neut eos lymph 7 = 'DENV1,DENV4 pos' mono baso; 8 = 'DENV2,DENV3 pos' format combdenv combdenv.; 9 = 'DENV2,DENV4 pos' run; 10 = 'DENV3,DENV4 pos' proc freq data=disser; 11 = 'DENV1,DENV2,DENV3 pos'

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12 = 'DENV1,DENV3,DENV4 pos' 3 = '2-11 yrs' 13 = 'DENV2,DENV3,DENV4 pos'; 4 = '12-18 yrs' value combdenv 5 = '19-44 yrs' 0 = 'DENV neg' 6 = '45+ yrs'; 1 = 'DENV1 pos, only' value serology 2 = 'DENV2 pos, only' 0 = 'missing' 3 = 'DENV3 pos, only' 1 = 'IgM pos' 4 = 'DENV4 pos, only' 2 = 'IgG pos' 5 = 'combined DENV'; 3 = 'IgM & IgG pos'; value pos value highlow 0 = 'negative' 0 = 'high' 1 = 'positive'; 1 = 'low'; value multipos value lowhigh 0 = 'mono inf' 0 = 'low' 1 = 'multi inf'; 1 = 'high'; value yesno value symptoms 0 = 'no' 0 = 'no symptoms' 1 = 'yes'; 1 = '<4 days ill' value sex 2 = '4+ days'; 0 = 'female' data disser; 1 = 'male'; set new.new_jen_data; value denvpos if denvpos = . then delete; -1 = 'missing' ************************************ 0 = 'negative' * change character to numeric 1 = 'positive'; * reset . to blank value cities ************************************; 1 = 'OCAÑA' if DAYSSX = '.' then DAYSSX = ' '; 2 = 'PATIOS' DAYSSX_n = DAYSSX*1; 3 = 'OTHER'; ******************************* value response * agecat variable 1 = 'missing data' *******************************; 2 = 'data available'; if age lt 1 then agecat = 1; value singcomb if age ge 1 and age lt 10 then agecat = 2; 0 = 'denv neg' if age ge 10 and age lt 18 then agecat = 3; 1 = 'denv single positive' if age ge 18 and age lt 30 then agecat = 4; 2 = 'denv multi positive'; if age ge 30 and age lt 40 then agecat = 5; value sympdays if age ge 40 and age lt 50 then agecat = 6; 1 = '<4 days ill' if age ge 50 and age lt 65 then agecat = 7; 2 = '4+ days'; if age ge 65 then agecat=8; value altage **************************************** 1 = '<1 yr' * dichotomous age 2 = '1 yr' ****************************************;

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if age lt 18 then agetwo = 0; ********************************* if age ge 18 then agetwo = 1; * combdenv ****************************************** *********************************; * altage combdenv = 0; ******************************************; if denvpos = . then combdenv=.; if age lt 1 then altage = 1; if denv1 = 1 then combdenv=1; if age ge 1 and age lt 2 then altage = 2; if denv2 = 1 or denv2 = 2 then if age ge 2 and age lt 12 then altage = 3; combdenv=2; if age ge 12 and age lt 19 then altage = 4; if denv3 = 1 then combdenv=3; if age ge 19 and age lt 45 then altage = 5; if denv4 = 1 then combdenv=4; if age ge 45 then altage = 6; if serodenv ge 5 then combdenv=5; *********************************** *************************************** * fix error in denv2 * neg, single pos, comb pos ***********************************; ***************************************; if denv2 = 2 then denv2 = 1; if combdenv = 0 then sing_comb = 0; ********************************** if combdenv ge 1 and combdenv le 4 then * serotype DENV sing_comb = 1; **********************************; if combdenv eq 5 then sing_comb = 2; serodenv = 0; ************************************** if denvpos = . then serodenv = .; * sex variable if denv1 = 1 then serodenv=1; **************************************; if denv2 = 1 then serodenv=2; if sex = 'F' then sex = 0; if denv3 = 1 then serodenv=3; if sex = 'M' then sex = 1; if denv4 = 1 then serodenv=4; ************************************* if denv1 = 1 and (denv2 = 1 or denv2 = 2) * fix denvpos and denv then serodenv=5; *************************************; if denv1 = 1 and denv3 = 1 then if denvpos = . then denvpos = -1; serodenv=6; if denv1 = . then denv1 = -1; if denv1 = 1 and denv4 = 1 then if denv2 = . then denv2 = -1; serodenv=7; if denv3 = . then denv3 = -1; if denv2 = 1 and denv3 = 1 then if denv4 = . then denv4 = -1; serodenv=8; ************************************* if denv2 = 1 and denv4 = 1 then * cities variable serodenv=9; *************************************; if denv3 = 1 and denv4 = 1 then if (city = 'ABREGO' or city = 'BOGOTA' or serodenv=10; city = 'CHICANOTA' or if denv1 = 1 and denv2 = 1 and denv3 = 1 city = 'COCHIRA' or city = then serodenv=11; 'CONVENCION' or city = 'CUCUTA' or if denv1 = 1 and denv3 = 1 and denv4 = 1 city = 'EL CARMEN' or city = then serodenv=12; 'GONZALES' or city = 'GUAMALITO' or if denv2 = 1 and denv3 = 1 and denv4 = 1 city = 'HACARI' or city = 'LA then serodenv=12; ESPERANZA' or city = 'LA PLAYA' or

128

city = 'RIO CESAR' or city = 'SAN multi_denv = 0; PABLO' or city = 'TARRA' or if serodenv ge 5 then multi_denv = 1; city = 'TEORAMA') then cities = 3; ******************************************* if city = 'OCAÑA' then cities = 1; * indicator variable for cities if city = 'PATIOS' then cities = 2; ********************************************; ************************************** city1 = 0; * response rate information if cities = 1 then city1 = 1; **************************************; city2 = 0; if denvpos = -1 then sero_response = 1; if cities = 2 then city2 = 1; if denvpos ge 0 then sero_response = 2; ************************************** if admit = . then clin_response = 1; * symptom days * if admit ge 0 then clin_response = 2; **************************************; if rbc = . then blood_response = 1; if dayssx_n eq . then symp_days = 0; if rbc ge 0 then blood_response = 2; if dayssx_n ge 0 and dayssx_n lt 6 then ************************************* symp_days=1; * fix gingbleed if dayssx_n ge 6 then symp_days=2; *************************************; **************************************** if gingbleed = -1 then gingbleed = .; * symptom days cut at the median = 3 days if gingbleed = 2 then gingbleed = 1; ****************************************; **************************************** if dayssx_n ge 0 and dayssx_n lt 4 then * serology sympdays2 = 1; ****************************************; if dayssx_n ge 4 then sympdays2 = 2; serology = 0; ****************************************** if igm = 1 then serology = 1; * account for missing if igg = 1 then serology = 2; *****************************************; if igm = 1 and igg = 1 then serology = 3; sympdays3 = sympdays2; ******************************************** if dayssx_n = . then sympdays3 = 0; * indicator variables for denv ******************************************** ********************************************; * indicator variables denv_one = 0; ********************************************; if denv1 = 1 then denv_one = 1; symptom1 = 0; denv_two = 0; if sympdays3 = 1 then symptom1 = 1; if denv2 = 1 then denv_two = 1; symptom2 = 0; denv_three = 0; if sympdays3 = 2 then symptom2 = 1; if denv3 = 1 then denv_three = 1; ******************************************** denv_four = 0; ***; if denv4 = 1 then denv_four = 1; * note: rbc missing 126 denv_comb = 0; ********************************************; if serodenv ge 5 then denv_comb = 1; /* sing_denv = 0; if rbc lt 5.0 then rbc2 = 1; if serodenv ge 1 and serodenv lt 5 then if rbc ge 5.0 then rbc2 = 0; sing_denv = 1; */

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******************************************* ******************************************** * plt cut at 100 (approx median) with 0 = plt * lymph cut at 40.0 (median) with 0 = gt 100 lymph ge 40.0 ********************************************; ********************************************; if plt ge 0 and plt lt 100 then plt2 = 1; if lymph ge 0 and lymph lt 40.0 then if plt ge 100 then plt2 = 0; lymph2 = 1; ******************************************** if lymph ge 40.0 then lymph2 = 0; * HB cut at 13 (approx median) with 0 = ******************************************* 13+ * note: mono missing 115 ********************************************; *******************************************; if hb ge 0 and hb lt 13 then hb2 = 1; ******************************************** if hb ge 13 then hb2 = 0; * note: baso missing 126 ******************************************** ********************************************; * hct cut at 40 (median) with 0 = hct ge 40 *********************************** ********************************************; * subset data to principal components data if hct ge 0 and hct lt 40.0 then hct2 = 1; set if hct ge 40.0 then hct2 = 0; * continuous variables clinical plus age ******************************************** **************************************; * wbc cut at 4.0 (median) with 0 = wbc ge data principal; 4.0 set disser; ******************************************** keep dayssx_n plt hb hct wbc neut lymph *********; age; if wbc ge 0 and wbc lt 4.0 then wbc2 = 1; run; if wbc ge 4.0 then wbc2 = 0; ******************************************* if wbc ge 0 and wbc le 5.0 then wbc3 = 1; * signs and symptoms for combdenv if wbc gt 5.0 then wbc3 = 0; *******************************************; ******************************************** proc freq data=disser; * alternate designation of case status for tables myalgia gingbleed vomit icterus wbc2 retropain jntpain hyperemia ********************************************; rash oligur petech diarrhea nosebleed if wbc2 = 1 then wbc_alt = 0; abdpain ha hypotn tachy; if wbc2 = 0 then wbc_alt = 1; format myalgia gingbleed vomit icterus ******************************************** retropain jntpain hyperemia ********** rash oligur petech diarrhea nosebleed * neut cut at 55 (median) with 0 = neut ge abdpain ha hypotn 55 tachy yesno.; ********************************************; run; if neut ge 0 and neut lt 55 then neut2 = 1; proc freq data=disser; if neut ge 55 then neut2 = 0; tables (myalgia gingbleed vomit icterus *************************** retropain jntpain hyperemia * note: EOS missing 56 rash oligur petech diarrhea nosebleed ***************************; abdpain ha hypotn

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tachy)*combdenv; run; format combdenv combdenv. myalgia proc anova data=disser; gingbleed vomit icterus retropain jntpain class combdenv; hyperemia model hct = combdenv; rash oligur petech diarrhea nosebleed means combdenv; abdpain ha hypotn means combdenv/lsd; tachy yesno.; format combdenv combdenv.; run; run; endsas; proc anova data=disser; ******************************************** class combdenv; * means of clinical variables by denv pos or model wbc = combdenv; neg means combdenv; ********************************************; means combdenv/lsd; proc sort data=disser; format combdenv combdenv.; by denvpos; run; run; proc anova data=disser; proc means data=disser; class combdenv; var dayssx_n plt hb hct wbc neut lymph age model neut = combdenv; rbc; means combdenv; run; means combdenv/lsd; proc ttest data=disser; format combdenv combdenv.; class denvpos; run; var dayssx_n plt hb hct wbc neut lymph age proc anova data=disser; rbc; class combdenv; format denvpos denvpos.; model lymph = combdenv; run; means combdenv; proc sort data=disser; means combdenv/lsd; by combdenv; format combdenv combdenv.; run; run; proc anova data=disser; ******************************************** class combdenv; * analysis of denv1 and wbc * model plt = combdenv; ********************************************; means combdenv; proc freq data=disser; means combdenv/lsd; tables combdenv*wbc_alt; format combdenv combdenv.; format combdenv combdenv. wbc_alt run; lowhigh.; proc anova data=disser; proc freq data=disser; class combdenv; tables agetwo*wbc_alt; model hb = combdenv; tables sex*wbc_alt; means combdenv; tables symp_days*wbc_alt; means combdenv/lsd; tables sympdays3*wbc_alt; format combdenv combdenv.; tables cities*wbc_alt;

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format wbc_alt lowhigh. agetwo agetwo. sex model wbc_alt(event='low') = sex; sex. cities cities. format wbc_alt lowhigh. denv_one sympdays3 symptoms.; denv_two denv_three denv_four denv_comb proc sort data=disser; pos. sex sex.; by combdenv; title 'crude odds ratio for sex covariate'; proc anova data=disser; run; class combdenv; proc logistic data=disser; model wbc = combdenv; model wbc_alt(event='low') = sympdays2; means combdenv; format wbc_alt lowhigh. denv_one means combdenv/lsd; denv_two denv_three denv_four denv_comb format combdenv combdenv.; pos. sympdays2 sympdays.; run; title 'crude odds ratio for sympdays proc sort data=disser; covariate'; by sympdays3; run; run; proc logistic data=disser; proc anova data=disser; model wbc_alt(event='low') = symp_days; class sympdays3; format wbc_alt lowhigh. denv_one model wbc = sympdays3; denv_two denv_three denv_four denv_comb means sympdays3; pos. sympdays2 sympdays.; means sympdays3/lsd; title 'crude odds ratio for sympdays format sympdays3 symptoms.; covariate'; run; run; proc logistic data=disser; proc logistic data=disser; model wbc_alt(event='low') = denv_one model wbc_alt(event='low') = symptom1 denv_two denv_three denv_four symptom2; denv_comb; format wbc_alt lowhigh. denv_one format wbc_alt lowhigh. denv_one denv_two denv_three denv_four denv_comb denv_two denv_three denv_four denv_comb pos. symp_days sympdays.; pos.; title 'crude odds ratio for sympdays title 'model of wbc and denv'; covariate'; run; run; *********************************** proc logistic data=disser; * adjust for other variables model wbc_alt(event='low') = city1 city2; ***********************************; format wbc_alt lowhigh. denv_one proc logistic data=disser; denv_two denv_three denv_four denv_comb model wbc_alt(event='low') = agetwo; pos.; format wbc_alt lowhigh. denv_one title 'crude odds ratio for cities covariate'; denv_two denv_three denv_four denv_comb run; pos. agetwo agetwo.; proc logistic data=disser; title 'crude odds ratio for age covariate'; model wbc_alt(event='low') = denv_one run; denv_two denv_three denv_four denv_comb proc logistic data=disser; agetwo;

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format wbc_alt lowhigh. denv_one format wbc_alt lowhigh. denv_one denv_two denv_three denv_four denv_comb denv_two denv_three denv_four denv_comb pos. agetwo agetwo.; pos. symp2days sympdays.; title 'model of wbc and denv adjust for age'; title 'model of wbc and denv adjust for run; symptom days'; proc logistic data=disser; run; model wbc_alt(event='low') = denv_one ******************************************** denv_two denv_three denv_four denv_comb * principal components analysis on sex; continuous data format wbc_alt lowhigh. denv_one ********************************************; denv_two denv_three denv_four denv_comb proc corr data=principal; pos. sex sex.; title 'principal components analysis'; title 'model of wbc and denv adjust for sex'; run; run; proc princomp data=principal; proc logistic data=disser; run; model wbc_alt(event='low') = denv_one proc factor data=principal simple corr; denv_two denv_three denv_four denv_comb run; symp_days; ******************************************** format wbc_alt lowhigh. denv_one * analysis of IgM, IGG, IgM and IgG by denv_two denv_three denv_four denv_comb days symptomology pos.; ********************************************; title 'model of wbc and denv adjust for * check distribution of clinical variables symptom days'; ********************************************; run; /* proc logistic data=disser; proc univariate freq data=disser; model wbc_alt(event='low') = denv_one var RBC PLT HB HCT WBC NEUT EOS denv_two denv_three denv_four denv_comb LYMPH MONO BASO; symptom1 symptom2; run; format wbc_alt lowhigh. denv_one proc univariate freq data=disser; denv_two denv_three denv_four denv_comb var hb; pos. symp2days sympdays.; run; title 'model of wbc and denv adjust for */ symptom days'; proc sort data=disser; run; by denvpos; ******************************************** proc means data=disser; * model with all covariates class denvpos; ********************************************; var dayssx_n; proc logistic data=disser; format denvpos denvpos.; model wbc_alt(event='low') = denv_one title 'analysis of IgM, IGG, IgM and IgG by denv_two denv_three denv_four denv_comb days symptom and denvpos'; symptom1 symptom2 run; agetwo sex; */

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/* */ proc sort data=disser; ****************************************** by denvpos igm; * tables plt2 hb2 hct2 wbc2 neut2 lymph2 proc means data=disser; ******************************************; by igm; proc freq data=disser; class denvpos; tables combdenv*plt2; var dayssx_n; tables combdenv*hb2; format denvpos denvpos. igm pos.; tables combdenv*hct2; run; tables combdenv*wbc2; proc sort data=disser; tables sing_comb*wbc2; by denvpos igg; tables combdenv*wbc3; proc means data=disser; tables combdenv*wbc_alt; by igg; tables combdenv*neut2; class denvpos; tables combdenv*lymph2; var dayssx_n; format combdenv combdenv. sing_comb format denvpos denvpos. igg pos.; singcomb. plt2 hb2 hct2 wbc2 neut2 lymph2 run; highlow. wbc_alt highlow.; */ run; /* ******************************************** proc sort data=disser; * logistic regression by denvpos serology; ********************************************; run; proc logistic data=disser; proc freq data=disser; model plt2(event='low') = denv_one tables denvpos serology; denv_two denv_three denv_four format denvpos denvpos. serology serology.; denv_comb; proc freq data=disser; format plt2 highlow. denv_one denv_two tables denvpos serology; denv_three denv_four denv_comb pos.; format denvpos denvpos. serology serology.; title 'model of plt and denv'; proc means data=disser; run; by denvpos; proc logistic data=disser; class serology; model hb2(event='low') = denv_one var dayssx_n; denv_two denv_three denv_four format denvpos denvpos. serology serology.; denv_comb; run; format hb2 highlow. denv_one denv_two proc anova data=disser; denv_three denv_four denv_comb pos.; by denvpos; title 'model of hb and denv'; class serology; run; model dayssx_n = serology; proc logistic data=disser; means serology; model hct2(event='low') = denv_one means serology/lsd; denv_two denv_three denv_four format denvpos denvpos. serology serology.; denv_comb; run;

134

format hct2 highlow. denv_one denv_two denv_two denv_three denv_four denv_comb denv_three denv_four denv_comb pos.; pos.; title 'model of hct and denv'; title 'model of wbc and denv'; run; run; proc logistic data=disser; proc logistic data=disser; model neut2(event='low') = denv_one model wbc2 = sing_denv multi_denv; denv_two denv_three denv_four format wbc2 highlow. denv_one denv_two denv_comb; denv_three denv_four denv_comb format neut2 highlow. denv_one denv_two sing_denv multi_denv pos.; denv_three denv_four denv_comb pos.; title 'model of wbc and denv'; title 'model of neut and denv'; run; run; proc logistic data=disser; proc logistic data=disser; model plt2 = sing_denv multi_denv; model lymph2(event='low') = denv_one format wbc2 highlow. denv_one denv_two denv_two denv_three denv_four denv_three denv_four denv_comb denv_comb; sing_denv multi_denv pos.; format lymph2 highlow. denv_one title 'model of wbc and denv'; denv_two denv_three denv_four denv_comb run; pos.; proc logistic data=disser; title 'model of lymph and denv'; model hb2 = sing_denv multi_denv; run; format wbc2 highlow. denv_one denv_two endsas; denv_three denv_four denv_comb proc logistic data=disser; sing_denv multi_denv pos.; model wbc2(event='high') = denv_one title 'model of wbc and denv'; denv_two denv_three denv_four run; denv_comb; proc logistic data=disser; format wbc2 highlow. denv_one denv_two model hct2 = sing_denv multi_denv; denv_three denv_four denv_comb pos.; format wbc2 highlow. denv_one denv_two title 'model of wbc and denv'; denv_three denv_four denv_comb run; sing_denv multi_denv pos.; proc logistic data=disser; title 'model of wbc and denv'; model wbc3 = denv_one denv_two run; denv_three denv_four denv_comb; proc logistic data=disser; format wbc3 highlow. denv_one denv_two model neut2 = sing_denv multi_denv; denv_three denv_four denv_comb pos.; format wbc2 highlow. denv_one denv_two title 'model of wbc and denv'; denv_three denv_four denv_comb run; sing_denv multi_denv pos.; proc logistic data=disser; title 'model of wbc and denv'; model wbc_alt(event='low') = denv_one run; denv_two denv_three denv_four proc logistic data=disser; denv_comb; model lymph2 = sing_denv multi_denv; format wbc_alt lowhigh. denv_one

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format wbc2 highlow. denv_one denv_two ************************************** denv_three denv_four denv_comb * symptom days sing_denv multi_denv pos.; **************************************; title 'model of wbc and denv'; symp_days = 0; run; if dayssx ge 4 then symp_days=1; proc logistic data=disser; data complete; model neut2 = denv_one denv_two set positive; denv_three denv_four denv_comb; if vomit = . then delete; format neut2 highlow. denv_one denv_two run; denv_three denv_four denv_comb pos.; proc univariate freq data=positive; title 'model of neut and denv'; var age; run; title 'for denv positive only'; proc logistic data=disser; run; model lymph2 = denv_one denv_two proc freq data=positive; denv_three denv_four denv_comb; tables agecat altage; format lymph2 highlow. denv_one format agecat agecat. altage altage.; denv_two denv_three denv_four denv_comb run; pos.; proc means data=positive; title 'model of lymph and denv'; var age; run; run; endsas; proc freq data=positive; ******************************************** tables sex city; * construct logistic regression models format sex sex.; ********************************************; run; data no_miss_denv; proc freq data=positive; set disser; tables multi_pos; if denvpos = -1 then delete; format multi_pos multipos.; data no_miss_all; run; set no_miss_denv; proc freq data=positive; if admit = . then delete; tables multi_pos*serodenv; run; format multi_pos multipos. serodenv data single; serodenv.; set disser; run; if combdenv = 5 then delete; endsas; data positive; /* set disser; proc contents position data=disser; if denvpos = 0 then delete; */ ************************************** proc freq data=disser; * single or multiple pos tables serodenv; **************************************; tables agecat; multi_pos = 0; tables sex; if serodenv ge 5 then multi_pos = 1; tables agecat*denvpos;

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tables city; run; tables city*denvpos; proc sort data=complete; tables city*serodenv; by myalgia; format serodenv serodenv. agecat agecat. run; denvpos denvpos. sex sex.; proc freq data=complete; run; tables myalgia*multi_pos/all; proc freq data=complete; format multi_pos multipos.; tables multi_pos; run; run; proc sort data=complete; proc sort data=complete; by retropain; by sex; run; run; proc freq data=complete; proc freq data=complete; tables retropain*multi_pos/all; tables sex*multi_pos/all fisher; format multi_pos multipos.; format sex sex. multi_pos multipos.; run; run; proc sort data=complete; /* by jntpain; proc logistic data=positive; run; model multi_pos = sex; proc freq data=complete; format multi_pos multipos. sex sex.; tables jntpain*multi_pos/all; run; format multi_pos multipos.; */ run; proc sort data=complete; proc sort data=complete; by vomit; by rash; run; run; proc freq data=complete; proc freq data=complete; tables vomit*multi_pos/all; tables rash*multi_pos/all; format multi_pos multipos. vomit yesno.; format multi_pos multipos.; run; run; /* proc sort data=complete; proc logistic data=positive; by abdpain; model multi_pos = vomit; run; format multi_pos multipos. vomit yesno.; proc freq data=complete; run; tables abdpain*multi_pos/all; */ format multi_pos multipos.; proc sort data=complete; run; by symp_days; proc sort data=complete; run; by ha; proc freq data=complete; run; tables symp_days*multi_pos/all; proc freq data=complete; format symp_days sympdays. multi_pos tables ha*multi_pos/all; multipos.; format multi_pos multipos.;

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run; run; proc sort data=complete; proc sort data=complete; by larvae; by icterus; run; run; proc freq data=complete; proc freq data=complete; tables larvae*multi_pos/all; tables icterus*multi_pos/all; format multi_pos multipos.; format multi_pos multipos.; run; run; proc sort data=complete; proc sort data=complete; by diarrhea; by hyperemia; run; run; proc freq data=complete; proc freq data=complete; tables diarrhea*multi_pos/all; tables hyperemia*multi_pos/all; format multi_pos multipos.; format multi_pos multipos.; run; run; proc sort data=complete; proc sort data=complete; by diarrhea; by OLIGUR; run; proc freq data=complete; proc freq data=complete; tables oligur*multi_pos/all; tables diarrhea*multi_pos/all; format multi_pos multipos.; format multi_pos multipos.; run; run; proc sort data=complete; proc sort data=complete; by petech; by fever; proc freq data=complete; run; tables petech*multi_pos/all; proc freq data=complete; format multi_pos multipos.; tables fever*multi_pos/all; run; format multi_pos multipos.; proc sort data=complete; run; by nosebleed; proc sort data=complete; proc freq data=complete; by gingbleed; tables nosebleed*multi_pos/all; run; format multi_pos multipos.; proc freq data=complete; run; tables gingbleed*multi_pos/all; proc sort data=complete; format multi_pos multipos.; by hypotn; run; proc freq data=complete; proc sort data=complete; tables hypotn*multi_pos/all; by agetwo; format multi_pos multipos.; run; run; proc freq data=complete; proc sort data=complete; tables agetwo*multi_pos/all; by tachy; format multi_pos multipos. agetwo agetwo.; proc freq data=complete;

138

tables tachy*multi_pos/all; title 'data for rbc, baso, plt - missing format multi_pos multipos.; denvpos taken out only'; run; proc freq data=no_miss_all; proc sort data=complete; tables sex city cities dayssx_n ADMIT by bednet; DENVCLASS FEVER MYALGIA proc freq data=complete; GINGBLEED VOMIt ICTERUS tables bednet*multi_pos/all; RETROPAIN JNTPAIN HYPEREMIA format multi_pos multipos.; RASH OLIGUR PETECH DIARRHEA run; NOSEBLEED ABDPAIN HA endsas; HYPOTN TACHY BEDNET LARVAE ******************************************* LARVLOC DENVPOS DENV1 DENV2 * response information DENV3 DENV4 agecat; *******************************************; format agecat agecat. denvpos denv1 denv2 proc freq data=disser; denv3 denv4 denvpos. cities cities.; tables sero_response clin_response title 'complete data'; blood_response; run; tables sero_response*clin_response; /* tables sero_response*blood_response; proc means data=disser; tables blood_response*clin_response; var age DENVPOS DENV1 DENV2 format sero_response clin_response DENV3 DENV4 IGM IGG LEPTO RBC blood_response response.; PLT HB HCT WBC NEUT EOS LYMPH run; MONO BASO DAYSSX_n; ******************************************** title 'data including missing denvpos'; * frequencies with complete data proc freq data=disser; ********************************************; tables sex city dayssx_n ADMIT ******************************************** DENVCLASS FEVER MYALGIA * all data including missing GINGBLEED VOMIT ICTERUS ********************************************; RETROPAIN JNTPAIN HYPEREMIA proc means data=disser; RASH OLIGUR PETECH DIARRHEA var age DENVPOS DENV1 DENV2 NOSEBLEED ABDPAIN HA DENV3 DENV4 IGM IGG LEPTO rbc HYPOTN TACHY BEDNET LARVAE PLT HB HCT WBC NEUT EOS LYMPH LARVLOC DENVPOS DENV1 DENV2 MONO BASO DAYSSX_n; DENV3 DENV4 agecat cities; title 'all data including missing'; format agecat agecat. denvpos denv1 denv2 proc means data=no_miss_all; denv3 denv4 denvpos. cities cities.; var age DENVPOS DENV1 DENV2 run; DENV3 DENV4 IGM IGG LEPTO PLT */ HB HCT WBC NEUT EOS LYMPH *************************************** MONO DAYSSX_n; * frequencies according to denvpos title 'complete data'; including combined denv proc means data=no_miss_denv; ***************************************; var rbc baso plt; proc sort data=no_miss_all;

139

by denvpos; format denvpos pos. sex sex.; run; run; proc freq data=no_miss_all; proc sort data=no_miss_all; tables (sex cities)*denvpos/chisq; by vomit; format denvpos pos. cities cities.; run; title 'complete data'; proc freq data=no_miss_all; run; tables vomit*denvpos/all; proc ttest data=no_miss_all; format denvpos pos. vomit yesno.; class denvpos; run; var age HB HCT WBC NEUT EOS proc logistic data=no_miss_all; LYMPH MONO; model denvpos = vomit; format denvpos pos.; format denvpos pos. vomit yesno.; title 'complete data'; run; run; ******************************************** proc sort data=no_miss_denv; * single, multiple denv positive by denvpos; ********************************************; proc ttest data=no_miss_denv; proc sort data=no_miss_all; class denvpos; by sing_comb; var rbc plt baso; run; format denvpos pos.; proc freq data=no_miss_all; title 'data for rbc, baso, and plt - missing tables (sex cities)*sing_comb/chisq; denvpos taken out'; format sing_comb singcomb. cities cities.; proc freq data=no_miss_all; title 'complete data - single and multiple tables (cities FEVER MYALGIA DENV pos'; GINGBLEED VOMIt ICTERUS run; RETROPAIN JNTPAIN HYPEREMIA proc anova data=no_miss_all; RASH OLIGUR PETECH DIARRHEA class sing_comb; NOSEBLEED ABDPAIN HA model age=sing_comb; HYPOTN TACHY BEDNET means sing_comb; LARVAE)*denvpos/chisq; means sing_comb/lsd; format denvpos pos. cities cities.; format sing_comb singcomb.; title 'complete data'; title 'Unadjusted ANOVA (sing_comb), run; mean age'; proc sort data=no_miss_all; run; by sex; proc anova data=no_miss_all; run; class sing_comb; proc freq data=no_miss_all; model hb=sing_comb; tables sex*denvpos/all; means sing_comb; format sex sex. denvpos pos.; means sing_comb/lsd; run; format sing_comb singcomb.; proc logistic data=no_miss_all; title 'Unadjusted ANOVA (sing_comb), model denvpos = sex; mean hb';

140 run; format sing_comb singcomb.; proc anova data=no_miss_all; title 'Unadjusted ANOVA (sing_comb), class sing_comb; mean lymph'; model hct=sing_comb; run; means sing_comb; proc anova data=no_miss_all; means sing_comb/lsd; class sing_comb; format sing_comb singcomb.; model mono=sing_comb; title 'Unadjusted ANOVA (sing_comb), means sing_comb; mean hct'; means sing_comb/lsd; run; format sing_comb singcomb.; proc anova data=no_miss_all; title 'Unadjusted ANOVA (sing_comb), class sing_comb; mean mono'; model wbc=sing_comb; run; means sing_comb; proc sort data=no_miss_denv; means sing_comb/lsd; by sing_comb; format sing_comb singcomb.; run; title 'Unadjusted ANOVA (sing_comb), proc anova data=no_miss_denv; mean wbc'; class sing_comb; run; model rbc=sing_comb; proc anova data=no_miss_all; means sing_comb; class sing_comb; means sing_comb/lsd; model neut=sing_comb; format sing_comb singcomb.; means sing_comb; title 'Unadjusted ANOVA (sing_comb), means sing_comb/lsd; mean rbc'; format sing_comb singcomb.; run; title 'Unadjusted ANOVA (sing_comb), proc anova data=no_miss_denv; mean neut'; class sing_comb; run; model plt=sing_comb; proc anova data=no_miss_all; means sing_comb; class sing_comb; means sing_comb/lsd; model eos=sing_comb; format sing_comb singcomb.; means sing_comb; title 'Unadjusted ANOVA (sing_comb), means sing_comb/lsd; mean platelet'; format sing_comb singcomb.; run; title 'Unadjusted ANOVA (sing_comb), proc anova data=no_miss_denv; mean eos'; class sing_comb; run; model baso=sing_comb; proc anova data=no_miss_all; means sing_comb; class sing_comb; means sing_comb/lsd; model lymph=sing_comb; format sing_comb singcomb.; means sing_comb; title 'Unadjusted ANOVA (sing_comb), means sing_comb/lsd; mean baso';

141

run; */ endsas; /* /* *************************************** *************************************** * frequencies according to combdenv * frequencies according to denvpos only singular individual denv1-4 singular ***************************************; ***************************************; proc sort data=single; proc sort data=single; by combdenv; by denvpos; run; run; proc freq data=single; proc freq data=single; tables (sex city hood)*combdenv/chisq; tables (sex city hood)*denvpos/chisq; format combdenv combdenv.; format denvpos pos.; title 'frequencies according to singular title 'frequencies according to singular denvpos'; denvpos'; run; run; proc anova data=single; proc ttest data=single; class combdenv; class denvpos; model age=combdenv; var age RBC PLT HB HCT WBC NEUT means combdenv; EOS LYMPH MONO BASO; means combdenv/lsd; format denvpos pos.; format combdenv combdenv.; title 'ttest by covariates according to title 'Unadjusted ANOVA (combdenv), singular denvpos'; mean age'; run; run; proc freq data=single; proc anova data=single; tables (FEVER MYALGIA GINGBLEED class combdenv; VOMIt ICTERUS RETROPAIN model rbc=combdenv; JNTPAIN HYPEREMIA RASH OLIGUR means combdenv; PETECH DIARRHEA NOSEBLEED means combdenv/lsd; ABDPAIN HA HYPOTN TACHY format combdenv combdenv.; BEDNET LARVAE)*denvpos/chisq; title 'Unadjusted ANOVA (combdenv), format denvpos pos. FEVER MYALGIA mean rbc'; GINGBLEED VOMIt ICTERUS run; RETROPAIN JNTPAIN HYPEREMIA proc anova data=single; RASH OLIGUR PETECH DIARRHEA class combdenv; NOSEBLEED ABDPAIN HA model plt=combdenv; HYPOTN TACHY BEDNET LARVAE means combdenv; yesno.; means combdenv/lsd; title 'frequencies according to singular format combdenv combdenv.; denvpos'; title 'Unadjusted ANOVA (combdenv), run; mean plt'; endsas; run;

142 proc anova data=single; title 'Unadjusted ANOVA (combdenv), class combdenv; mean eos'; model hb=combdenv; run; means combdenv; proc anova data=single; means combdenv/lsd; class combdenv; format combdenv combdenv.; model lymph=combdenv; title 'Unadjusted ANOVA (combdenv), means combdenv; mean hb'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=single; title 'Unadjusted ANOVA (combdenv), class combdenv; mean lymph'; model hct=combdenv; run; means combdenv; proc anova data=single; means combdenv/lsd; class combdenv; format combdenv combdenv.; model mono=combdenv; title 'Unadjusted ANOVA (combdenv), means combdenv; mean hct'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=single; title 'Unadjusted ANOVA (combdenv), class combdenv; mean mono'; model wbc=combdenv; run; means combdenv; proc anova data=single; means combdenv/lsd; class combdenv; format combdenv combdenv.; model baso=combdenv; title 'Unadjusted ANOVA (combdenv), means combdenv; mean wbc'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=single; title 'Unadjusted ANOVA (combdenv), class combdenv; mean baso'; model neut=combdenv; run; means combdenv; proc freq data=single; means combdenv/lsd; tables (FEVER MYALGIA GINGBLEED format combdenv combdenv.; VOMIt ICTERUS RETROPAIN title 'Unadjusted ANOVA (combdenv), JNTPAIN HYPEREMIA RASH OLIGUR mean neut'; PETECH DIARRHEA NOSEBLEED run; ABDPAIN HA HYPOTN TACHY proc anova data=single; BEDNET LARVAE)*combdenv/chisq; class combdenv; format combdenv combdenv. FEVER model eos=combdenv; MYALGIA GINGBLEED VOMIt means combdenv; ICTERUS RETROPAIN JNTPAIN means combdenv/lsd; HYPEREMIA RASH OLIGUR PETECH format combdenv combdenv.; DIARRHEA NOSEBLEED ABDPAIN HA

143

HYPOTN TACHY BEDNET LARVAE means combdenv/lsd; yesno.; format combdenv combdenv.; title 'frequencies according to singular title 'Unadjusted ANOVA (combdenv), denvpos'; mean plt, positives only'; run; run; endsas; proc anova data=positive; */ class combdenv; *************************************** model hb=combdenv; * frequencies according to individual denv1- means combdenv; 4 among positives only means combdenv/lsd; ***************************************; format combdenv combdenv.; proc sort data=positive; title 'Unadjusted ANOVA (combdenv), by combdenv; mean hb, positives only'; run; run; proc freq data=positive; proc anova data=positive; tables (sex city hood)*combdenv/chisq; class combdenv; format combdenv combdenv.; model hct=combdenv; title 'frequencies according to singular means combdenv; denvpos, positives only'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=positive; title 'Unadjusted ANOVA (combdenv), class combdenv; mean hct, positives only'; model age=combdenv; run; means combdenv; proc anova data=positive; means combdenv/lsd; class combdenv; format combdenv combdenv.; model wbc=combdenv; title 'Unadjusted ANOVA (combdenv), means combdenv; mean age, positives only'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=positive; title 'Unadjusted ANOVA (combdenv), class combdenv; mean wbc, positives only'; model rbc=combdenv; run; means combdenv; proc anova data=positive; means combdenv/lsd; class combdenv; format combdenv combdenv.; model neut=combdenv; title 'Unadjusted ANOVA (combdenv), means combdenv; mean rbc, positives only'; means combdenv/lsd; run; format combdenv combdenv.; proc anova data=positive; title 'Unadjusted ANOVA (combdenv), class combdenv; mean neut, positives only'; model plt=combdenv; run; means combdenv; proc anova data=positive;

144

class combdenv; format combdenv combdenv. FEVER model eos=combdenv; MYALGIA GINGBLEED VOMIt means combdenv; ICTERUS RETROPAIN JNTPAIN means combdenv/lsd; HYPEREMIA RASH OLIGUR PETECH format combdenv combdenv.; DIARRHEA NOSEBLEED ABDPAIN HA title 'Unadjusted ANOVA (combdenv), HYPOTN TACHY BEDNET LARVAE mean eos, positives only'; yesno.; run; title 'frequencies according to singular proc anova data=positive; denvpos, positives only'; class combdenv; run; model lymph=combdenv; endsas; means combdenv; proc freq data=disser; means combdenv/lsd; tables agecat denvpos denv1 denv2 denv3 format combdenv combdenv.; denv4 serodenv combdenv; title 'Unadjusted ANOVA (combdenv), format agecat agecat. serodenv serodenv. mean lymph, positives only'; combdenv combdenv.; run; run; proc anova data=positive; proc freq data=disser; class combdenv; tables city*denvpos; model mono=combdenv; tables sex*denvpos; means combdenv; run; means combdenv/lsd; proc sort data=disser; format combdenv combdenv.; by denvpos; title 'Unadjusted ANOVA (combdenv), run; mean mono, positives only'; proc means; run; class denvpos; proc anova data=positive; var age rbc plt hb hct wbc neut eos lymph class combdenv; mono baso; model baso=combdenv; run; means combdenv; proc freq data=disser; means combdenv/lsd; tables denvpos*(serodenv combdenv); format combdenv combdenv.; format serodenv serodenv. combdenv title 'Unadjusted ANOVA (combdenv), combdenv.; mean baso, positives only'; run; run; /* proc freq data=positive; proc freq data=disser; tables (FEVER MYALGIA GINGBLEED tables denv2*(denv1 denv3 denv4 VOMIt ICTERUS RETROPAIN serodenv); JNTPAIN HYPEREMIA RASH OLIGUR format serodenv serodenv.; PETECH DIARRHEA NOSEBLEED run; ABDPAIN HA HYPOTN TACHY */ BEDNET LARVAE)*combdenv/chisq; proc sort data=disser;

145

by combdenv; run; proc means data=disser; class combdenv; var age rbc plt hb hct wbc neut eos lymph mono baso; format combdenv combdenv.; run; proc freq data=disser; tables combdenv*sex; format combdenv combdenv.; /* proc univariate freq data=disser; var age; */ run; endsas;

146

APPENDIX I COLOMBIA SEROPREVALENCE SURVEY RAW DATA

SEX AGE MUNICIPALITY DENV DENV1 DENV2 DENV3 DENV4 IGM IGG LEPTO DATEDRAW ERYTH PLT HB HCT LEU NEUT EOS LYMPH MONO BASO DPO ADMIT MYALGIA GINGBLEED VOMIT ICTERUS RETROPAIN JNTPAIN HYPEREMIA RASH OLIGUR PETECH DIARRHEA NOSEBLEED ABDPAIN HA HYPOTN TACHY F 9 OCAÑA 1 1 0 0 0 0 0 0 11/05/13 4.4 108 11.5 36.2 1.7 72.3 0.6 21.8 4.5 0.8 ...... F 9 SAN PABLO 1 0 1 0 0 0 0 0 11/24/13 5.7 13 16.0 47.9 5.5 66.4 1.2 26.5 5.1 0.8 ...... M 10 PATIOS 1 0 0 1 1 0 1 0 12/12/13 . 110 12.9 41.0 2.7 57.0 . 43.0 . . 4 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 M 10 PATIOS 0 0 0 0 0 . . 0 04/07/14 . 142 11.8 37.0 7.2 20.0 6.0 74.0 . . 7 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 F 10 PATIOS 1 0 1 0 0 0 1 0 12/03/13 . 137 11.0 36.0 4.0 16.0 5.0 79.0 . . 6 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 F 10 PATIOS 1 0 1 0 0 0 1 0 09/24/13 . 71 14.0 44.1 4.9 20.0 . 80.0 . . 5 1 1 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 M 10 PATIOS 0 0 0 0 0 . . 0 03/31/14 . 86 11.8 37.0 2.6 40.0 2.0 58.0 . . 3 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 M 10 PATIOS 0 0 0 0 0 . . 0 04/07/14 . 145 11.7 38.0 1.7 30.0 . 70.0 . . 2 1 0 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 F 10 OCAÑA 0 0 0 0 0 0 0 0 12/01/13 4.7 110 12.5 38.1 3.6 78.2 3.3 20.0 3.8 0.2 ...... M 10 CONVENCION 0 0 0 0 0 0 0 0 12/16/13 5.1 178 13.8 41.1 7.6 21.0 1.3 72.2 5.1 0.4 ...... M 10 TEORAMA 1 0 1 0 0 0 1 0 02/19/14 5.4 72 14.6 43.7 3.9 43.1 0.1 38.3 17.7 0.8 ...... M 11 PATIOS 1 0 0 1 0 0 1 0 12/24/13 . 138 12.1 41.0 1.8 41.0 2.0 57.0 . . 2 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 M 11 PATIOS 1 1 0 0 0 0 1 0 10/21/13 . 92 11.7 36.7 4.1 75.0 2.0 23.0 . . 3 1 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 F 11 CONVENCION 0 0 0 0 0 0 0 0 11/26/13 5.1 96 12.1 37.4 3.6 66.6 1.1 27.3 4.8 0.2 ...... F 11 OCAÑA 0 0 0 0 0 0 0 0 10/31/13 5.6 159 13.3 41.9 2.5 65.9 1.2 30.3 2.5 0.1 ...... F 11 OCAÑA 1 0 1 0 0 0 0 0 01/31/13 4.9 25 14.9 42.4 2.6 77.3 1.8 16.6 4.2 0.1 ...... M 11 OCAÑA 1 0 1 0 0 0 0 0 10/29/13 4.9 32 13.7 40.9 3.1 72.3 0.9 17.7 8.6 0.5 ...... M 11 PATIOS 1 1 0 0 0 . . 0 04/24/14 . 140 13.5 44.0 2.3 60.0 . 40.0 . . 4 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 F 11 PATIOS 0 0 0 0 0 0 1 0 10/24/13 . 100 12.9 41.1 2.8 65.0 . 35.0 . . 2 1 1 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 M 12 PATIOS 0 0 0 0 0 2 2 0 03/27/14 . 95 11.9 37.0 1.8 33.0 2.0 65.0 . . 6 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 M 12 PATIOS 0 0 0 0 0 0 1 0 10/17/13 . 114 11.3 35.4 11.0 37.0 1.0 59.0 3.0 . 3 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 M 12 OCAÑA 1 1 0 1 1 0 1 0 03/12/14 5.1 104 15.2 45.0 2.5 58.2 1.5 37.1 2.8 0.4 ...... F 12 PATIOS 0 0 0 0 0 0 1 0 01/23/14 . 138 13.3 43.0 3.1 30.0 . 70.0 . . 3 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 F 12 PATIOS 0 0 0 0 0 0 1 0 11/20/13 . 94 12.8 40.0 2.6 46.0 . 54.0 . . 3 0 1 0 1 0 0 0 0 1 0 1 0 1 1 1 0 0 F 12 TARRA 1 1 0 0 0 0 0 0 10/24/13 4.9 29 12.8 39.8 6.7 89.6 2.5 7.2 0.6 0.1 ...... M 13 PATIOS 0 0 0 0 0 1 1 0 03/14/14 . 102 11.0 36.0 3.9 34.0 1.0 65.0 . . 5 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 F 13 PATIOS 1 0 1 0 0 . . 0 04/13/14 . 139 10.1 35.0 2.3 72.0 . 28.0 . . 4 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 M 13 GONZALES 0 0 0 0 0 0 1 0 02/22/14 4.9 106 13.8 39.7 4.5 85.0 0.0 9.0 5.8 0.2 ...... F 13 PATIOS 0 0 0 0 0 0 1 0 12/28/13 . 71 12.1 39.0 3.0 12.0 2.0 86.0 . . 8 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 F 13 PATIOS 0 0 0 0 0 0 1 0 10/21/13 . 114 12.2 38.2 2.2 56.0 5.0 39.0 . . 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 M 13 PATIOS 1 0 1 1 0 . . 0 04/01/14 . 149 13.5 43.0 3.6 50.0 . 50.0 . . 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 F 14 PATIOS 0 0 0 0 0 . . 0 04/03/14 . 105 NHR 37.0 3.0 20.0 4.0 76.0 . . 5 0 1 0 1 0 1 1 0 1 0 0 0 0 0 1 0 0 M 14 CONVENCION 1 0 1 0 1 0 0 0 12/30/13 5.8 47 16.0 47.9 5.1 94.1 0.6 2.5 2.5 0.3 ...... F 14 CONVENCION 0 0 0 0 0 0 0 0 11/26/13 4.3 31 11.8 36.1 9.6 57.2 0.8 31.7 9.2 1.1 ...... F 14 PATIOS 0 0 0 0 0 . . 0 04/10/14 . 97 12.4 39.0 5.1 25.0 1.0 74.0 . . 5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 M 14 OCAÑA 1 0 0 0 1 0 0 0 10/22/13 5.3 32 15.3 45.2 4.7 76.9 3.1 15.8 3.8 0.4 ...... M 14 SAN PABLO 1 0 1 0 0 0 0 0 01/15/14 5.8 49 15.5 48.3 4.9 52.7 3.6 37.0 6.5 0.2 ...... M 14 PATIOS 1 1 0 0 0 . . 0 04/09/14 . 106 11.6 38.0 2.0 54.0 . 46.0 . . 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 M 14 TEORAMA 1 0 1 0 0 0 0 0 11/10/13 5.6 15 16.1 47.8 3.2 87.6 1.1 5.7 4.7 0.9 ...... F 14 PATIOS 0 0 0 0 0 1 1 0 03/03/14 . 108 12.8 42.0 2.7 37.0 2.0 61.0 . . 3 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 M 14 PATIOS 0 0 0 0 0 0 1 0 09/13/13 . 152 13.7 42.9 3.0 34.0 6.0 60.0 . . 4 0 1 0 1 0 1 1 0 1 0 0 0 0 0 1 0 0 M 14 TEORAMA 1 0 1 0 0 0 0 0 11/21/13 5.0 40 13.2 42.4 4.6 49.2 2.6 45.7 2.5 0.0 ...... M 14 PATIOS 0 0 0 0 0 0 1 0 01/10/14 . 112 14.3 45.0 4.7 45.0 2.0 53.0 . . 3 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 F 15 OCAÑA 0 0 0 0 0 0 0 0 10/23/13 5.0 36 14.0 40.9 4.6 72.9 1.6 23.1 2.4 0.0 ...... F 15 OCAÑA 1 0 1 0 0 0 0 0 12/22/13 5.3 19 14.6 45.4 4.3 42.4 2.4 47.7 7.5 0.0 ...... M 15 OCAÑA 1 0 1 0 0 0 1 0 03/18/14 5.1 120 14.4 42.6 8.2 40.9 1.1 46.8 11.0 0.2 ...... M 15 PATIOS 0 0 0 0 0 0 1 0 09/27/13 . 106 14.8 46.2 4.3 39.0 2.0 59.0 . . 3 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 M 15 PATIOS 1 1 1 1 0 0 1 0 10/03/13 . 110 13.0 40.0 1.3 52.0 . 48.0 . . 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 F 15 OCAÑA 0 0 0 0 0 0 0 0 10/21/13 5.6 25 15.7 46.3 3.9 88.1 1.6 8.7 1.4 0.2 ...... M 15 PATIOS 0 0 0 0 0 0 1 0 10/30/13 . 74 14.2 44.9 3.3 35.0 1.0 64.0 . . 3 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 F 15 PATIOS 1 0 1 0 0 . . 0 04/10/14 . 88 39.4 12.0 1.8 69.0 . 31.0 . . 4 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 M 15 OCAÑA 0 0 0 0 0 0 0 0 12/30/13 5.7 43 15.3 46.4 4.7 82.5 1.2 12.5 3.7 0.1 ...... M 16 PATIOS 0 0 0 0 0 0 1 0 10/24/13 . 119 14.0 44.2 3.7 30.0 . 70.0 . . 3 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 F 16 PATIOS 1 1 0 0 0 0 1 0 10/30/13 . 110 13.8 45.3 11.3 38.0 . 62.0 . . 3 0 1 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 M 16 ABREGO 0 0 0 0 0 0 0 0 11/07/13 5.7 42 16.3 49.8 4.1 86.7 1.1 10.4 1.8 0.0 ...... M 16 PATIOS 0 0 0 0 0 . . 0 04/23/14 . 126 11.9 40.0 3.1 50.0 1.0 49.0 . . 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 F 16 PATIOS 0 0 0 0 0 0 1 1 09/27/13 . 106 13.8 43.9 2.6 63.0 8.0 29.0 . . 3 0 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 F 16 PATIOS 1 0 1 0 0 . . 0 04/24/14 . 100 11.7 37.0 2.1 72.0 . 28.0 . . 2 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 M 16 PATIOS 1 0 0 1 0 0 1 0 . 121 15.1 48 3.4 71.0 4.0 25.0 . . 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 F 16 OCAÑA 0 0 0 0 0 0 0 0 12/06/13 4.8 75 13.3 40.5 1.8 84.6 1.7 10.1 3.3 0.3 ...... M 17 PATIOS 1 0 0 0 1 . . 0 04/22/14 . 109 14.3 44.0 3.6 62.0 1.0 37.0 . . 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 F 17 ABREGO 0 0 0 0 0 0 0 0 10/30/13 5.1 90 12.4 39.8 3.2 86.0 2.0 11.3 0.6 0.1 ...... F 17 OCAÑA 1 1 0 0 0 0 0 0 03/05/14 4.1 73 12.1 35.2 2.3 81.4 3.1 9.7 5.7 0.1 ...... F 17 OCAÑA 0 0 0 0 0 0 0 0 12/19/13 4.7 118 13.1 40.8 4.6 59.8 1.7 33.1 5.0 0.4 ...... F 17 PATIOS 1 1 0 0 0 0 1 0 10/17/13 . 96 12.9 42.7 2.8 46.0 2.0 52.0 . . 2 1 1 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 M 18 OCAÑA 0 0 0 0 0 0 0 0 11/06/13 4.2 137 12.5 37.9 8.8 91.9 1.1 4.3 1.8 0.9 ...... F 19 ABREGO 1 1 0 0 0 0 0 0 11/29/13 5.3 38 15.6 44.5 8.0 46.4 1.8 41.3 9.1 1.4 ...... F 19 TEORAMA 0 0 0 0 0 0 0 0 11/09/13 4.8 15 13.7 41.8 3.0 70.4 0.8 25.6 3.2 0.0 ...... F 19 OCAÑA 1 0 1 0 0 0 0 0 11/08/13 4.7 118 13.7 41.6 2.8 84.8 0.9 10.2 4.0 0.1 ...... M 19 PATIOS 1 0 0 1 0 0 1 0 01/11/14 . 132 14.0 43.0 2.6 38.0 . 62.0 . . 6 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 F 20 PATIOS 0 0 0 0 0 0 1 0 09/17/13 . 145 12.2 38.1 4.5 29.0 1.0 70.0 . . 3 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 147

SEX AGE MUNICIPALITY DENV DENV1 DENV2 DENV3 DENV4 IGM IGG LEPTO DATEDRAW ERYTH PLT HB HCT LEU NEUT EOS LYMPH MONO BASO DPO ADMIT MYALGIA GINGBLEED VOMIT ICTERUS RETROPAIN JNTPAIN HYPEREMIA RASH OLIGUR PETECH DIARRHEA NOSEBLEED ABDPAIN HA HYPOTN TACHY M 1 PATIOS 0 0 0 0 0 . . 0 04/10/14 . 81 11.1 38.0 6.1 16.0 . 84.0 . . 3 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 M 1 OCAÑA 0 0 0 0 0 0 0 0 11/25/13 5.3 127 13.6 41.6 2.7 78.8 0.9 18.4 1.8 0.1 ...... F 1 OCAÑA 1 0 0 1 0 0 0 0 11/10/13 4.9 23 13.2 39.4 5.6 47.0 0.4 49.1 3.4 0.1 ...... M 1 PATIOS 1 0 1 0 1 . . 0 04/01/14 . 132 10.2 33.0 6.1 27.0 1.0 72.0 . . 3 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 M 1 OCAÑA 0 0 0 0 0 0 0 0 11/05/13 4.3 17 10.8 32.1 4.0 43.1 1 44.8 10.8 0.3 ...... F 1 OCAÑA 0 0 0 0 0 0 0 0 11/10/13 4.8 70 14.1 42.5 3.1 74.0 0.8 19.8 5.1 0.3 ...... M 1 PATIOS 1 0 1 0 0 0 1 0 01/28/14 . 130 38.1 12.0 4.0 10.0 1.0 89.0 . . 3 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 F 1 HACARI 0 0 0 0 0 0 0 0 10/19/14 4.3 147 11.4 35.0 3.7 80.1 1.8 15.5 2.6 0.0 ...... M 1 PATIOS 0 0 0 0 0 0 1 0 01/29/14 . 144 10.7 35.0 2.5 17.0 . 83.0 . . 2 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 F 1 LA PLAYA 0 0 0 0 0 0 0 0 02/07/14 4.9 162 12.3 36.7 9.8 17.7 0.0 66.8 14.5 1.0 ...... M 1 PATIOS 0 0 0 0 0 0 1 0 01/27/14 . 142 10.3 35.0 5.3 24.0 . 76.0 . . 2 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 F 1 PATIOS 0 0 0 0 0 1 1 0 02/26/14 . 131 12.6 39.0 8.6 54.0 . 46.0 . . 3 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 M 1 PATIOS 0 0 0 0 0 0 1 0 . . 105 11.9 39.5 5.6 39.0 3.0 58.0 . . 4 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 F 2 GUAMALITO 1 0 0 1 0 0 0 0 12/21/13 4.9 151 12.3 38.4 3.5 77.7 0.3 18.9 2.1 1.0 ...... M 2 PATIOS 0 0 0 0 0 0 1 0 01/05/14 . 62 11.9 37.0 3.1 30.0 3.0 67.0 . . 4 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 M 2 PATIOS 1 0 1 1 0 0 1 0 09/17/14 . 75 12.5 38.0 5.0 42.0 . 58.0 . . 3 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 F 2 PATIOS 0 0 0 0 0 1 1 0 02/20/14 . 117 11.0 35.0 6.0 17.0 2.0 81.0 . . 9 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 F 3 PATIOS 0 0 0 0 0 0 1 0 09/20/13 . 92 11.2 35.8 2.5 40.0 8.0 52.0 . . 5 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 F 3 PATIOS 1 0 0 1 0 1 1 0 03/12/14 . 99 10.0 33.0 3.3 36.0 2.0 62.0 . . 3 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 M 3 SAN PABLO 1 0 0 1 1 0 0 0 01/12/14 5.3 58 13.4 42.5 4.7 64.7 1.5 30.8 2.8 0.2 ...... M 3 PATIOS 0 0 0 0 0 . . 0 04/01/14 . 124 10.5 34.0 4.0 14.0 1.0 85.0 . . 4 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 F 3 PATIOS 0 0 0 0 0 0 1 0 12/02/13 . 78 9.2 29.0 2.9 27.0 . 73.0 . . 2 1 1 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 M 3 PATIOS 0 0 0 0 0 0 1 0 01/22/14 . 102 8.7 30.0 5.1 10.0 . 90.0 . . 3 0 1 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 F 4 PATIOS 1 1 0 0 0 0 1 0 11/18/13 . 144 10.4 32.0 3.0 28.0 . 72.0 . . 3 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 M 4 PATIOS 0 0 0 0 0 2 1 0 03/22/14 . 149 12.9 41.0 4.8 56.0 . 44.0 . . 4 1 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 F 4 PATIOS 1 0 1 0 1 . . 0 04/21/14 . 174 10.6 36.0 3.8 21.0 . 79.0 . . 3 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 M 4 ABREGO 0 0 0 0 0 0 0 0 11/21/13 5.4 57 13.6 41.4 9.6 45.9 0.7 51.3 2.0 0.1 ...... M 5 TEORAMA 1 0 1 0 0 0 0 0 11/05/13 5.9 33 13.9 43.2 5.5 66.3 0.7 25.2 7.7 0.1 ...... F 5 PATIOS 0 0 0 0 0 0 1 0 11/24/13 . 144 11.3 37.0 9.8 21.0 1.0 76.0 2.0 . 7 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 F 5 PATIOS 1 1 0 0 0 1 0 0 02/04/14 . 133 13.3 41.0 2.7 34.0 . 66.0 . . 3 0 1 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 F 5 OCAÑA 0 0 0 0 0 0 0 0 10/20/13 5.0 115 13.4 40.9 2.2 60.8 2.4 33.1 3.6 0.1 ...... F 5 OCAÑA 0 0 0 0 0 0 0 0 10/28/13 5.0 19 12.2 38.7 12.2 88.6 0.6 9.6 1.1 0.1 ...... M 5 PATIOS 0 0 0 0 0 0 1 0 01/10/14 . 128 9.7 31.0 4.1 32.0 2.0 66.0 . . 3 0 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 F 5 PATIOS 0 0 0 0 0 1 1 0 02/24/14 . 109 10.2 34.0 4.7 32.0 1.0 67.0 . . 6 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 M 5 PATIOS 1 1 0 0 0 0 1 0 11/05/13 . 83 11.5 35.8 3.7 29.0 . 71.0 . . 4 1 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 M 5 OCAÑA 0 0 0 0 0 0 0 0 12/29/13 4.2 84 12.3 35.6 2.6 71.5 1.6 25.5 1.3 0.1 ...... F 5 CUCUTA 1 0 1 0 0 . . 0 04/29/14 . 145 11.8 39.0 4.8 26.0 . 74.0 . . 5 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 M 6 PATIOS 1 0 1 0 0 . . 0 04/09/14 . 128 10.9 36.0 2.7 21.0 1.0 77.0 1.0 . 6 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 M 6 PATIOS 1 0 0 1 0 2 1 0 03/24/14 . 142 11.2 37.0 4.6 35.0 12.0 53.0 . . 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 M 7 PATIOS 1 0 0 1 0 0 1 0 11/15/13 . 133 11.4 36.0 1.4 30.0 . 70.0 . . 4 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 M 7 PATIOS 0 0 0 0 0 0 1 0 01/01/14 . 95 11.7 38.0 1.0 35.0 3.0 62.0 . . 2 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 M 7 PATIOS 1 0 0 1 0 0 1 0 01/29/14 . 125 10.5 35.0 6.1 14.0 . 86.0 . . 6 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 M 7 PATIOS 1 0 0 1 0 0 1 0 01/28/14 . 101 13.5 42.0 3.7 32.0 . 68.0 . . 3 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 F 7 OCAÑA 0 0 0 0 0 0 0 0 03/18/14 4.2 125 12.1 36.1 3.8 11.1 3.1 77.7 7.5 0.6 ...... F 7 PATIOS 0 0 0 0 0 0 1 0 10/24/13 . 70 11.4 38.3 3.4 31.0 6.0 63.0 . . 5 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 F 7 PATIOS 1 0 1 0 0 0 1 0 10/18/13 . 96 11.8 37.5 2.9 66.0 4.0 30.0 . . 4 0 0 0 1 0 0 0 0 1 0 1 1 1 1 1 0 0 M 7 OCAÑA 0 0 0 0 0 0 0 0 10/13/13 4.2 129 12.3 36.4 3.7 90.1 1.5 6.9 1.3 0.2 ...... F 7 PATIOS 0 0 0 0 0 0 1 0 12/08/13 . 80 11.7 37.0 4.0 20.0 19.0 60.0 1.0 . 6 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 M 7 PATIOS 0 0 0 0 0 0 1 0 09/13/13 . 137 11.2 35.6 2.5 47.0 12.0 41.0 . . 4 0 1 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 M 7 CHICANOTA 1 1 0 0 0 0 1 0 11/04/13 . 110 11.9 39.2 5.4 14.0 1.0 84.0 1.0 . 3 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 F 8 PATIOS 1 1 1 0 0 1 1 0 02/08/14 . 99 14.3 45.0 2.3 60.0 . 40.0 . . 3 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 M 8 PATIOS 0 0 0 0 0 1 1 0 02/27/14 . 138 12.7 41.0 4.1 27.0 8.0 65.0 . . 7 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 F 8 PATIOS 1 0 0 1 0 0 1 0 12/17/13 . 128 11.7 37.0 3.1 44.0 . 56.0 . . 4 1 1 0 1 1 0 0 0 1 1 1 0 0 1 1 0 0 F 8 PATIOS 1 0 1 0 1 . . 0 04/15/14 . 131 11.4 37.0 3.0 67.0 . 33.0 . . 3 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 F 8 PATIOS 0 0 0 0 0 0 1 0 09/22/13 . 153 11.1 37.0 2.6 40.0 2.0 54.0 4.0 . 4 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 M 8 OCAÑA 0 0 0 0 0 0 0 0 10/22/13 5.2 110 14.1 41.2 2.8 63.2 2.9 25.9 7.9 0.1 ...... M 8 PATIOS 1 1 0 0 0 0 1 0 10/02/13 . 122 10.9 35.3 2.0 50.0 8.0 42.0 . . 3 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 F 8 PATIOS 1 0 1 0 0 0 1 0 10/19/14 . 127 13.0 40.0 2.5 62.0 2.0 36.0 . . 2 1 0 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 M 8 PATIOS 0 0 0 0 0 . . 0 04/13/14 . 125 12.9 5.0 2.9 40.0 4.0 56.0 . . 3 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 M 8 SAN PABLO 0 0 0 0 0 0 0 0 01/30/14 4.5 142 13.0 35.6 6.3 88.1 4.1 6.5 1.3 0.0 ...... M 9 PATIOS 1 0 1 0 1 . . 0 04/12/14 . 161 11.8 38.0 3.0 55.0 . 45.0 . . 3 1 1 0 1 0 1 0 0 1 0 0 0 0 1 1 0 0 F 9 PATIOS 1 0 0 0 1 0 1 0 10/21/13 . 122 11.8 37.3 3.4 44.0 . 56.0 . . 2 0 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 M 9 PATIOS 1 0 1 0 0 0 1 0 10/02/13 . 126 12.5 38.4 3.0 56.0 6.0 38.0 . . 2 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 F 9 PATIOS 1 0 1 0 0 0 1 0 09/24/13 . 134 13.5 42.9 2.9 61.0 3.0 36.0 . . 4 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 F 9 PATIOS 1 1 0 0 0 0 1 0 10/07/13 . 127 12.3 41.8 3.8 35.0 4.0 61.0 . . 8 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 M 9 PATIOS 1 0 0 0 1 0 1 0 10/22/13 . 135 12.1 36.7 2.6 59.0 1.0 40.0 . . 2 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 M 9 OCAÑA 1 0 0 1 0 0 0 0 01/04/13 4.8 127 12.8 40.6 3.3 92.1 1.4 4.9 1.5 0.1 ...... M 9 PATIOS 1 0 1 1 1 0 1 0 01/01/14 . 118 11.4 37.0 1.8 55.0 3.0 42.0 . . 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 F 9 OCAÑA 0 0 0 0 0 0 0 0 12/10/13 4.9 101 13.9 41.9 4.3 52.0 1.6 40.1 6.3 0.0 ...... M 9 PATIOS 0 0 0 0 0 0 1 0 11/13/13 . 72 10.5 35.0 2.2 56.0 . 44.0 . . 5 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 M 9 PATIOS 1 0 0 1 0 1 1 0 03/13/14 . 91 11.6 38.0 2.2 75.0 . 35.0 . . 3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 148

SEX AGE MUNICIPALITY DENV DENV1 DENV2 DENV3 DENV4 IGM IGG LEPTO DATEDRAW ERYTH PLT HB HCT LEU NEUT EOS LYMPH MONO BASO DPO ADMIT MYALGIA GINGBLEED VOMIT ICTERUS RETROPAIN JNTPAIN HYPEREMIA RASH OLIGUR PETECH DIARRHEA NOSEBLEED ABDPAIN HA HYPOTN TACHY M 20 OCAÑA 1 1 0 0 0 0 0 0 02/03/14 5.1 184 14.8 45.0 2.9 55.5 0.3 29.5 14.4 0.3 ...... M 21 PATIOS 1 0 1 0 0 . . 0 04/13/14 . 129 14.0 43.0 7.7 69.0 2.0 29.0 . . 3 0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 M 21 PATIOS 1 0 0 1 0 0 1 1 12/12/13 . 112 13.4 43.0 2.8 22.0 2.0 76.0 . . 6 1 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 M 22 OCAÑA 0 0 0 0 0 0 0 0 12/02/13 3.9 70 13.2 37.7 4.2 79.7 0.6 17.8 1.7 0.2 ...... F 22 OCAÑA 0 0 0 0 0 0 0 0 11/05/13 3.8 147 12.2 37.8 4.2 72.2 2.9 21.6 2.7 0.6 ...... M 22 EL CARMEN 1 0 0 1 0 0 0 0 12/31/13 5.3 161 14.5 45.3 2.7 83.2 1.5 11.3 3.3 0.7 ...... M 22 COCHIRA 1 0 1 0 0 0 0 0 02/06/14 5.9 12 19.1 53.3 7.3 22.5 2.8 63.8 10.4 0.5 ...... M 23 PATIOS 1 0 0 0 1 0 1 0 11/05/13 . 63 14.2 43.6 2.3 63.0 . 37.0 . . 4 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 M 23 PATIOS 1 0 0 0 1 . . 0 04/29/14 . 129 14.5 45.0 4.3 73.0 2.0 23.0 2.0 . 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 M 23 TEORAMA 0 0 0 0 0 0 0 0 11/26/13 5.5 41 16.2 49.2 7.0 83.7 1.4 12.9 1.9 0.1 ...... M 24 PATIOS 0 0 0 0 0 0 1 0 01/11/14 . 121 14.7 45.0 4.6 77.0 . 23.0 . . 3 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 F 25 PATIOS 1 1 0 0 0 0 1 0 09/27/13 . 134 10.0 33.1 2.0 32.0 6.0 62.0 . . 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 F 25 PATIOS 1 0 0 0 1 0 1 0 01/15/14 . 80 11.6 38.0 2.3 44.0 9.0 46.0 1.0 . 4 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 M 26 PATIOS 1 0 1 0 0 . . 0 04/12/14 . 40 13.1 45.0 3.2 58.0 . 42.0 . . 3 1 1 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 M 26 RIO CESAR 0 0 0 0 0 0 0 0 12/04/13 4.3 72 12.7 38.5 4.6 83.0 3.6 8.6 4.5 0.3 ...... F 27 OCAÑA 1 0 0 1 0 0 0 0 02/09/14 4.5 26 14.5 41.8 2.0 47.9 2.3 37.7 11.5 0.6 ...... F 28 ABREGO 0 0 0 0 0 0 0 0 11/28/13 5.0 78 12.7 38.6 1.6 56.9 1.7 39.0 2.4 0.0 ...... M 28.0 OCAÑA 0 0 0 0 0 0 0 0 12/03/13 4.9 191 14.0 41.0 7.6 76.5 6.4 14.0 3.0 0.1 ...... M 28 LA ESPERANZA 0 0 0 0 0 0 1 0 02/22/14 6.0 48 17.9 52.9 1.9 25.3 0.6 63.1 10.1 0.9 ...... F 28 TEORAMA 0 0 0 0 0 0 1 0 02/23/14 4.4 58 12.9 37.3 2.4 25.8 0.4 63.6 8.1 2.1 ...... F 30 PATIOS 1 0 0 0 1 0 1 0 01/11/14 . 132 12.5 39.0 5.2 64.0 2.0 34.0 . . 3 0 1 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 F 30 OCAÑA 0 0 0 0 0 0 0 0 10/26/13 4.6 112 13.2 41.6 2.0 75.1 2.4 20.7 1.8 0.0 ...... M 31 TEORAMA 1 0 1 0 0 0 0 0 12/19/13 6.5 11 17.7 54.6 4.9 63.5 2.9 26.4 7.0 0.2 ...... M 31 OCAÑA 1 1 0 0 0 0 0 0 12/19/13 6.0 66 18.1 55.6 3.1 69.0 1.0 23.7 6.1 0.2 ...... F 31 TEORAMA 1 0 1 0 0 0 1 0 02/23/14 4.4 9 13.3 39.9 2.3 20.7 2.6 66.0 7.9 2.8 ...... M 31 OCAÑA 0 0 0 0 0 0 0 0 12/30/13 5.6 94 15.6 44.9 2.4 64.8 3.2 25.4 6.3 0.3 ...... M 32 OCAÑA 0 0 0 0 0 0 0 0 12/02/13 5.5 88 15.8 47.5 6.0 83.2 1.8 11.2 2.7 1.1 ...... F 33 PATIOS 1 0 0 0 1 1 1 0 02/26/14 . 126 12.0 38.0 2.4 58.0 2.0 40.0 . . 4 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 M 33 PATIOS 1 0 1 0 0 0 1 1 09/30/13 . 117 12.6 38.8 4.4 85.0 . 15.0 . . 3 0 1 0 1 0 1 1 0 0 0 0 1 0 1 1 0 0 F 33 OCAÑA 1 1 0 0 0 0 0 0 01/28/14 4.5 38 15.4 41.7 4.1 65.0 1.5 31.0 2.3 0.2 ...... M 34 TEORAMA 0 0 0 0 0 0 0 0 11/21/13 5.1 44 14.8 43.8 4.5 58.7 4.5 34.3 2.4 0.1 ...... F 34 GUAMALITO 1 0 0 1 0 0 0 0 01/14/14 4.5 41 13.5 40.0 1.8 87.2 0.4 11.1 1.2 0.1 ...... M 34 SAN PABLO 1 1 0 0 0 0 0 0 01/20/14 5.4 94 15.9 47.0 1.5 81.1 0.3 16.0 2.3 0.3 ...... F 35 PATIOS 0 0 0 0 0 0 1 0 12/21/13 . 78 12.3 34.0 7.6 26.0 . 74.0 . . 6 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 M 35 OCAÑA 0 0 0 0 0 0 0 0 12/12/13 4.8 53 13.0 39.4 11.4 91.2 1.2 2.0 1.7 3.9 ...... F 36 PATIOS 1 0 0 1 0 0 1 0 10/12/13 . 123 12.2 40.6 5.4 38.0 1.0 61.0 . . 5 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 M 36 OCAÑA 0 0 0 0 0 0 0 0 10/18/13 5.4 51 15.6 46.7 2.6 80.7 4.1 10.1 4.8 0.3 ...... M 36 PATIOS 0 0 0 0 0 0 1 0 11/08/13 . 112 11.0 35.0 5.9 75.0 2.0 22.0 1.0 . 5 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 M 37 PATIOS 1 0 0 1 0 0 1 0 11/18/13 . 32 13.8 42.0 5.9 81.0 . 19.0 . . 2 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 F 37 SAN PABLO 1 0 1 0 1 0 0 0 01/17/13 4.8 55 14.8 41.5 9.6 32.8 5.4 55.1 5.8 0.9 ...... M 37 SAN PABLO 1 0 1 1 0 0 0 0 01/15/14 5.9 16 16.3 50.8 4.6 55.3 1.3 33.0 9.7 0.7 ...... F 38 PATIOS 0 0 0 0 0 0 1 0 11/18/13 . 80 11.6 39.0 4.4 21.0 2.0 77.0 . . 3 0 1 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 M 38 BOGOTA 0 0 0 0 0 0 0 0 02/28/14 4.7 90 15.2 44.0 2.6 37.8 2.4 49.2 9.2 1.4 ...... F 41 PATIOS 1 1 0 0 0 0 1 0 09/14/13 . 98 12.4 39.4 2.5 53.0 5.0 40.0 2.0 . 4 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 M 43 PATIOS 0 0 0 0 0 0 1 0 12/04/13 . 145 13.5 45.0 3.6 57.0 . 43.0 . . 4 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 M 43 PATIOS 0 0 0 0 0 0 1 0 12/04/13 . 145 13.5 45.0 3.6 37.0 1.0 62.0 . . 4 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 M 43 PATIOS 0 0 0 0 0 0 1 0 09/16/13 . 109 14.2 42.6 2.1 54.0 2.0 44.0 . . 5 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 M 45 PATIOS 1 0 0 1 0 0 1 0 10/12/13 . 150 15.6 48.8 3.4 50.0 5.0 45.0 . . 5 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 M 45 OCAÑA 0 0 0 0 0 0 0 0 11/03/13 6.1 128 17.5 52.5 4.0 73.3 2.1 20.1 4.3 0.2 ...... M 46 OCAÑA 0 0 0 0 0 0 0 0 12/25/13 4.8 119 13.4 40.3 5.2 86.4 1.2 8.6 3.6 0.2 ...... M 47 PATIOS 0 0 0 0 0 0 1 0 01/31/14 . 123 16.1 52.0 3.3 25.0 . 75.0 . . 4 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 M 48 PATIOS 1 0 1 0 0 0 1 0 01/05/14 . 86 15.5 50.0 3.6 36.0 6.0 56.0 2.0 . 4 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 F 49 OCAÑA 0 0 0 0 0 0 0 0 10/28/13 4.6 33 12.5 38.2 2.0 85.2 3.0 8.7 2.8 0.3 ...... F 50 PATIOS 0 0 0 0 0 1 1 0 02/20/14 . 125 11.5 38.0 5.6 74.0 . 26.0 . . 2 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 F 51 GUAMALITO 0 0 0 0 0 0 0 0 01/19/14 4.2 137 11.9 39.0 5.0 52.4 5.4 36.9 5.2 0.1 ...... M 51 PATIOS 1 0 0 1 0 0 1 0 12/28/13 . 130 . 38.0 7.4 73.0 . 27.0 . . 8 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 F 52 PATIOS 0 0 0 0 0 0 1 0 01/05/14 . 86 14.4 43.0 2.1 55.0 1.0 44.0 . . 4 1 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 M 54 OCAÑA 0 0 0 0 0 0 0 0 12/02/13 4.4 129 12.9 39.7 3.5 81.0 2.6 13.2 2.1 1.1 ...... M 54 OCAÑA 0 0 0 0 0 0 1 0 03/19/14 5.6 58 16.2 47.2 5.0 43.4 2.6 44.0 9.1 0.9 ...... M 55 OCAÑA 0 0 0 0 0 0 0 0 11/23/13 5.1 125 15.8 46.8 3.1 69.4 1.5 23.7 5.1 0.3 ...... F 56 EL CARMEN 0 0 0 0 0 0 0 0 12/03/13 4.4 24 12.1 36.0 1.7 78.6 1.3 16.6 3.1 0.4 ...... M 57 PATIOS 1 0 1 0 0 . . 0 04/29/14 . 109 8.8 30.0 9.0 91.0 . 9.0 . . 3 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 M 59 OCAÑA 0 0 0 0 0 0 0 0 12/16/13 5.2 76 16.2 46.3 3.5 68.5 1.5 27.6 2.3 0.1 ...... M 61 PATIOS 1 0 1 0 0 . . 0 03/31/14 . 123 12.4 40.0 5.3 59.0 . 41.0 . . 6 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 F 62 PATIOS 1 0 1 1 0 . . 0 03/29/14 . 97 11.6 37.0 3.8 62.0 2.0 36.0 . . 4 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 M 65 SAN PABLO 1 0 1 0 0 0 0 0 01/18/14 4.5 66 14.1 41.5 3.8 38.4 5.2 45.4 8.2 2.8 ...... F 73 OCAÑA 0 0 0 0 0 0 0 1 10/17/13 4.3 20 14.0 42.2 7.6 71.9 1.1 22.0 3.7 1.3 ...... F 76 PATIOS 0 0 0 0 0 1 1 0 02/05/14 . 119 11.0 39.0 4.7 74.0 . 26.0 . . 4 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 M 77 PATIOS 0 0 0 0 0 0 1 0 12/26/13 . 148 10.2 32.0 5.1 62.0 1.0 37.0 . . 3 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 F 80 PATIOS 1 0 0 1 0 1 1 0 02/09/14 . 124 12.1 39.0 5.7 65.0 . 35.0 . . 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 F 82 PATIOS 1 0 1 0 0 . . 0 04/15/14 . 123 9.3 31.0 10.5 85.0 . 15.0 . . 2 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 F 85 OCAÑA 0 0 0 0 0 0 0 0 11/02/13 5.1 49 14.5 43.3 11.4 94.5 1.6 2.2 0.9 0.8 ...... F 87 PATIOS 1 0 0 1 0 2 2 0 03/25/14 . 123 10.8 35.0 6.0 75.0 . 25.0 . . 3 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 F 92 PATIOS 0 0 0 0 0 . . 0 03/28/14 . 67 11.9 38.0 4.2 84.0 . 16.0 . . 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

149

APPENDIX E EBOLA VIRUS DISEASE STUDY IRB APPROVAL

ACTION ON PROTOCOL CONTINUATION REQUEST

Dr. Dennis Landin, Chair Institutional Review Board 130 David Boyd Hall Baton Rouge, LA 70803 P: 225.578.8692 F: 225.578.5983 [email protected] | lsu.edu/irb TO: Christopher Mores Pathobiological Sciences

FROM: Dennis Landin Chair, Institutional Review Board

DATE: June 10, 2016

RE: IRB# 3610

TITLE: Operations and research infrastructure augmentation plan for increased support to stop the transmission of the Ebola virus in Port Loko and improve public health response to disease future threats

New Protocol/Modification/Continuation: Continuation

Review type: Full Expedited X Review date: 6/10/2016

Risk Factor: Minimal X Uncertain Greater Than Minimal______

Approved X Disapproved______

Approval Date: 6/10/2016 Approval Expiration Date: 6/9/2017

Re-review frequency: (annual unless otherwise stated)

Number of subjects approved: 200

LSU Proposal Number (if applicable): 43128

Protocol Matches Scope of Work in Grant proposal: (if applicable)

By: Dennis Landin, Chairman

150

APPENDIX K POST-EBOLA VIRUS DISEASE STUDY CONSENT FORM

POST-EBOLA SYNDROME: INVESTIGATING THE CLINICAL PROFILE OF PAIN AND DISABILITY AMONG SURVIVORS OF EBOLA VIRUS DISEASE IN BOMI COUNTY, LIBERIA

Principal Investigator: Christopher Mores Co-investigator: Jennifer Giovanni

Description: You are a survivor of Ebola virus disease. Since you left the ETU, you have told us that you have pain, tiredness, and sadness. In February, I examined your body and asked you questions about your health. Now I ask your permission to do another physical exam and to ask you more questions about your health. The purpose of the physical exam and the questions is to learn where your body hurts, how your life has changed, and how nurses and doctors can learn about post-Ebola syndrome in people who survived Ebola.

Procedures: I will look at parts of your body, including your hands and feet, your neck, and your muscles. I will press on them to ask if they hurt. I will touch parts of your skin and ask you if you feel pain. I will ask you to move your arms and to walk. I will also take your blood pressure and ask you questions about how you feel. I will ask you if it is easy or hard to farm, to cook, and to walk in your village. I will ask how you got Ebola.

The examination will not go inside your body. I will not take your blood.

Risks and benefits: There is no risk to your body or health by being a part of this study. There is no benefit to you or your body by being part of this study. There is no treatment for Ebola or the pains people have when they survive Ebola. You will not get any treatment by being part of this study. The information I get from you will help nurses and doctors understand why survivors of Ebola have pain and tiredness. This will help us understand how to take care of people who survive Ebola in the future.

Privacy: You name will not be given to any person. The name of your village will not be given to any person. I know your name and village, and I will keep it private for just me. Information about how you got Ebola, your treatment, and the pain you have will be published. I will not give any information that will tell people who you are.

Time: This exam and the questions will take about 1 hour.

Payment: You will not get money for being in this study.

If you have questions: You can call me, Jennifer, on my Liberia mobile. The number is +231 88 618 7306. When I am in America, you can call me on my American mobile. The number in America is +001 310 948 2366.

Participant: I am signing this form because the study was told to me in a language I understand. I know that I will be asked questions about my body and when I had Ebola. I do not have to be in this study. If I want to

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be in this study, I will have a copy of this paper. I know there is no treatment for Ebola. I know my name and village name will not be given to anyone.

Photos (“snaps”): I may ask to take snaps of your exam. I will not take pictures of your face. If the snaps are used in the research, your name and face will not be in the snap.

By putting your name or mark below:

It is okay for snaps of me to be taken and published. ______

It is NOT okay for snaps of me to be taken and published. ______

______Your Name or Mark Your Date of Birth or Age

______Date of Exam Your Mobile Number

______Witness Printed Name or Mark

______Signature of Nurse Doing Your Exam Liberian Nursing License Number

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APPENDIX L FIELD INVESTIGATION NOTES, LIBERIA, JUNE 2015

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APPENDIX F HISTORY AND PHYSICAL EXAMINATION, HUSBAND, LIBERIA, 2015

General appearance is that of a well-appearing, 40 year-old, muscular African male. Posture is erect; appears thin for height/weight. Demeanor, facial expressions appropriate to clinical setting. Maintains eye contact. Intellect congruent w/ educational level. A&O x 3. Immediate, recent, remote memory intact.

PMI: (1) 2009 admission for blood transfusion s/t malaria, (2) 2003 surgery for repair of LLL bisection of tibialas anterior during the civil war.

MEDICATIONS: None.

ALLERGIES: Cotrimoxazole; reports acute, intense dermal itching.

FAMILY HISTORY: Older brother with diabetes. Mother has chronic lower back pain. No h/o HTN or vascular disease.

SKIN/NAILS: Integument smooth, intact. Soft, warm to touch. No edema, rashes. Nails firm, clean, smooth. Hands calloused, appropriate to occupation. No clubbing, cyanosis. Lateral aspect of RLL significant for 1-inch open lesion, circular in appearance. Patient states it is a recent wound r/t farming. Borders pink with evidence of epithelial regeneration. No pus or exudate with palpation.

HEAD AND NECK: General appearance is normal cephalic shape proportional to body. Hair closely shaven. Facial features symmetrical, appropriately placed. No pain reported upon palpation of skull. Lymph nodes, thyroid not palpated; no masses, swelling observed.

EYES: Eyelids are smooth, lashes appropriately placed. PERRLA. Conjunctivae pale w/o exudates; sclerae moist, white with intermittent brown coloration. Lacrimal ducts moist without tearing. Cornea smooth, transparent w/ symmetric light reflex. Six cardinal positions of gaze (CN III, IV, VI) intact w/ smooth movement. No nystagmus. Fundoscopic exam not performed. Peripheral fields not equal. Right eye has normal confrontation, peripheral visual fields, and distal acuity. Left eye has decreased confrontation, marked distal acuity deficit with inability to discern the number of fingers presented. Left peripheral deficits: inferior and superior ≈ 45 ̊ from transverse plane, nasal ≈ 30 ̊ from sagittal plane, and temporal ≈ 60 ̊ from sagittal plane.

ENT Ears: Pinnae symmetrical, appropriately placed on head. No pain upon palpation. Sensation equal bilaterally. Internal exam not performed. Nose: No discharge, obstruction. Septum is midline. Nares symmetrical, clear, patent. Internal exam, smelling test for CN I not performed. Mouth/pharynx: Lips dry, brown, symmetrical. Mucosa moist, pink, w/o lesions. Teeth present, clean; gums well-attached to dentitia. No pain with mastication. Swallow normal. Internal exam not performed.

THORAX AND LUNGS: Respirations unlabored, smooth. No cough, pain with normal/deep respiration. No boney abnormalities. Movement symmetrical w/ respiratory excursion. No use of accessory muscles. No audible rhonchi, wheezes or rubs. Jugular, carotid pulsations not visible. Auscultation, percussion not performed.

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CARDIOVASCULAR: RRR. No gallop, rub, or murmur.

ABDOMEN: Integument smooth, intact w/o lesions, color congruent w/ rest of body; normal hair distribution. Soft, flat, bowel sounds audible x 4 quadrants. Nontender to light, deep palpation. BS, liver not examined.

MUSCULOSKELETAL: Posture is erect and stable in sitting, standing, walking positions. Vertebral column straight w/o visible lordosis, kyposis. Head appears midline between shoulders. Muscle mass, tone equal bilaterally. Joints are defined w/o edema. Patient has full ROM of bilateral knees but demonstrates pain at extremes of flexion/extension. ROM of shoulders limited by pain at extension above shoulder level. Grip, UE strength strong, equal bilaterally; however, arthralgia of wrist, elbow, and shoulder joints made assessment difficult. Patient has extreme sensitivity to palpation over the lumbo-sacral area; retracts from slight pressure. From L3-L5, pain ascends the vertebral column to T7-T9. Patient indicates severe pain from C1-C7 with limited ROM of head in all four directions. Pain is concentrated over the splenius capitus and occipitalis muscles with a visible pain response upon palpation. For emphasis, the patient experiences relief from pain only when all the joints are in their most neutral positions without motion. Patient denies myalgias, although the muscle fibers in immediate proximity to the joints are mildly painful. In fact, it is the arthralgias that fatigue him, not muscular fatigue. All joints, including the fingers and toes, are painful, limiting the ability to perform DALYs, especially farming. When discussing his pain, the patient becomes visibly saddened.

NEUROLOGIC Mental status: The patient is alert, attentive, and oriented. Speech is clear and fluent with good repetition, comprehension, and recall. Motor: Patient moves all extremities. Coordination, accuracy of movement equal bilaterally, upper, lower. Gait even w/ alternating arm movement. No loss of balance. No tremor, abnormal or extraneous movements. Gait is steady with normal step, base, arm swing, and turning. Heel and toe walking are normal. Reflexes: Not examined. Sensory: Grossly normal to light touch, pressure. Senses are intact in fingers and toes. No neuropathy or paraesthesia.

Cranial Nerves CN II: Visual fields are full to confrontation. CN III, IV, VI: At primary gaze, there is no eye deviation. When the patient is looking to the left, the right eye does not adduct. When the patient is looking up, the right eye does not move up as well as the left. She develops horizontal diplopia in all directions of gaze especially when looking to the left. Convergence is impaired. CN V: Corneal responses intact bilaterally. Facial sensations intact on right side. Tactile sensation diminished over the three branches. CN VII: Face is symmetric with normal eye closure and smile. CN VII: Hearing is normal with to whispering. Answers questions appropriately. CN IX, X: Not examined. CN XI: Head turning intact. CN XII: Not examined.

GENITOURINARY: Not examined. Patient states he occasionally awakens with an erection of height and firmness equal to pre-EVD status. Sexual intercourse not attempted discharge from ETU. Rectal/prostate exam deferred.

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VITA

Jennifer Giovanni is an infectious disease epidemiologist whose career has incorporated bench research, clinical practice, public health policy, and global health field work. She has worked globally across academic, federal, military, and non-governmental milieux. She holds a Master of Public Health in infectious disease epidemiology from Emory University and a Master of Science in critical care and public health nursing from the University of California, Los Angeles. She served as a US Peace Corps volunteer and a commissioned officer with the US Public Health Service and the US Air Force.

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