Community Risk Factors for Post-Hurricane Disease

by Kate Hultberg

A THESIS

submitted to

Oregon State University

Honors College

in partial fulfillment of the requirements for the degree of

Honors Baccalaureate of Science in Public Health (Honors Associate)

Presented May 28, 2020 Commencement June 2020

AN ABSTRACT OF THE THESIS OF

Kate Hultberg for the degree of Honors Baccalaureate of Science in Public Health presented on May 28, 2020. Title: Community Risk Factors for Post-Hurricane Disease.

Abstract approved:______Viktor Bovbjerg

This thesis explores interrelationships between the geographical impact of recent hurricanes, rates of giardiasis and cryptosporidiosis in impacted communities, and effects of hurricanes on local water infrastructure systems. I obtained quantitative data on geographical hurricane attributes, demographic characteristics of populations struck by hurricanes, and confirmed cases of giardiasis and cryptosporidiosis within the time period of years 2000 to 2018.

Qualitative data was also retrieved through review of local and regional news outlets to identify issues with water quality following storms, particularly regarding water infrastructure of areas affected by hurricanes. All data was examined for overarching trends and corresponding associations between increases in waterborne disease and documented infrastructure damage in the year following a hurricane. There was no clear association between cases of giardiasis and infrastructure failures, but a potential relationship of cryptosporidiosis and infrastructure failures was observed. Results also indicated no clear association between being struck by a hurricane and income, social vulnerability index, or race. Areas lacking research include resources and capabilities of water facilities during environmental stress, additional variables resulting in upticks of disease, and financial resources of communities most at risk. This study revealed the need for more granular data and an increased focus on relationships between waterborne disease and water infrastructure.

Key Words: waterborne disease, hurricanes, infrastructure Corresponding e-mail address: [email protected]

©Copyright by Kate Hultberg May 28, 2020

Community Risk Factors for Post-Hurricane Disease

by Kate Hultberg

A THESIS

submitted to

Oregon State University

Honors College

in partial fulfillment of the requirements for the degree of

Honors Baccalaureate of Science in Public Health (Honors Associate)

Presented May 28, 2020 Commencement June 2020

Honors Baccalaureate of Science in Public Health project of Kate Hultberg presented on May 28, 2020.

APPROVED:

______Viktor Bovbjerg, Mentor, representing College of Public Health and Human Sciences

______Perry Hystad, Committee Member, representing College of Public Health and Human Sciences

______Michael Campana, Committee Member, representing College of Earth, Ocean, and Atmospheric Sciences

______Toni Doolen, Dean, Oregon State University Honors College

I understand that my project will become part of the permanent collection of Oregon State University, Honors College. My signature below authorizes release of my project to any reader upon request.

______Kate Hultberg, Author

Table of Contents

List of Figures and Tables 7 Figures 7 Tables 7

Introduction 8

Goal and Objectives 13

Methods 14 Hurricanes 14 Demographics 16 Diseases 17 Infrastructure 18 Analysis 20 Results 22 Hurricanes 22 Demographics 23 Diseases 25 Infrastructure 29

Discussion, Limitations, and Conclusions 34 Discussion 34 Limitations 35 Conclusions 37

Bibliography 39

Appendix A - FEMA Financial Assistance Designation Maps (FEMA-DR) 43

Appendix B - Waterborne Disease Trend Graphs 45

Appendix C - Infrastructure Sources 50

List of Figures and Tables

Figures

Figure Page

1. Percentage of FEMA-identified United States counties hit by one or more 22 hurricanes between 2000 and 2018......

2. Total giardiasis cases per state for Hurricane Sandy...... 26

3. Total giardiasis cases per state for Hurricanes Ike...... 27

4. Total cryptosporidiosis cases per state for Hurricane Ike...... 28

5. Total cryptosporidiosis cases per state for Hurricane Dolly...... 28

6. Percentage of reports in each of the three categories of infrastructure data. 29

Tables

Table Page

1. Saffir-Simpson scale of hurricane intensity...... 8

2. Attribute categories for organization of infrastructure data...... 19

3. Weighted state averages of income and SVI based on county population. . . 23

4. Weighted state averages of race based on county population...... 24

5. Observed trends in giardiasis and cryptosporidiosis cases after each 32 hurricane......

Introduction

For decades, hurricanes have been commonplace in North America, most frequently in the southeastern US. The Atlantic hurricane season typically occurs between June and

November and can consist of multiple named storms of varying severity per year between the

Gulf and Atlantic Coasts (NOAA, n.d.). In recent years, many have witnessed the devastating effects of large hurricanes such as Hurricane Katrina (2005) and Hurricane Maria (2017). Both

Katrina and Maria were classified as Category 5 storms, indicating that their maximum sustained wind speeds were above 157 miles per hour or higher (NOAA, n.d.). Further breakdown of windspeed and category affiliation are as follows in Table 1.

Category Wind Speed (mph) Damage Level

1 74-95 Minimal

2 96-110 Moderate

3 111-129 Extensive

4 130-156 Extreme

5 157 or higher Catastrophic

Table 1: Saffir-Simpson scale of hurricane intensity. Adapted from (NOAA, n.d.)

Any large named storm has the potential to bring catastrophic effects due to heavy rainfall, damage to infrastructure, and increase in public health risks. Current research suggests increases in both severity and prevalence of hurricanes in the near future. A study from 2019 by the Geophysical Fluid Dynamics Laboratory of NOAA suggests that there is a strong likelihood

9 of increases in precipitation rates and intensity due to anthropogenically-induced global warming and climate change (NOAA, 2020). This increase carries many direct and indirect risks to the human population, perhaps most notably our own health.

With increases in precipitation and hurricane intensities, communities can and should expect higher rates of flooding, damage, and water-related illnesses. After major climatic weather events, contaminated water and crippled water infrastructure caused by an inundation of water is a major concern. During the weeks after a significant storm, many locales will introduce supplemental water facility inspections, limit standard drinking water by enforcing boil water notices, and at times encourage homeowners to treat their water if it comes from a well or the city supply. These measures are typically precautionary as mass contaminations are not frequent, but are highly encouraged by government officials after any large-scale storm (Water Quality Association, n.d.). Nevertheless, damage to water delivery infrastructure and wastewater treatment facilities is common after hurricanes. For example, a wastewater plant was overwhelmed with floodwaters during Hurricane Florence in Conway, South Carolina. As a result, this plant released untreated wastewater into a local tributary, contaminating large volumes of water with bacteria-laden sewage (AP, 2018-a). This type of incident was also seen after Hurricane Ike in 2008 when weakened water infrastructure began overflowing raw sewage into the streets of

Galveston, Texas (Urbina, 2008). Mass contamination events such as these allude to a major environmental health hazard that has the potential to expose both entire communities and individuals to harmful toxins and waterborne diseases.

Contaminated waters and extreme weather events create an environment suitable for pathogens to thrive. Heavy rains, flooding, and the subsequent water infrastructure overburden have been linked to various waterborne diseases such as cholera, Hepatitis A, and giardiasis

(Lemery & Auerbach, 2017). Waterborne pathogens are separated into three main categories: bacteria, parasites, and viruses. In this paper, parasitic pathogens will be further explored, as they are less commonly studied while being vastly prevalent globally. Types of parasites can be further broken up into three more subcategories: protozoa, helminths, and ectoparasites (CDC, 2019-a). Of the parasites that are transmitted primarily through water, protozoan parasitic infections are far more commonly seen in developed nations such as the United States (Haque, 2007). Perhaps the two most prevalent protozoan parasites, Giardia and Cryptosporidium, lead to approximately 1.2 million and 748,000 annual U.S. cases, respectively (Benedict et al., 2019). Both are microscopic, single-celled organisms that have the ability to critically damage gastrointestinal health in humans. Both Giardia and Cryptosporidium are nationally-listed notifiable diseases that are tracked through the Waterborne Disease and Outbreak Surveillance

System (WBDOSS). When local cases of giardiasis or cryptosporidiosis arise, the WBDOSS’s objective is to monitor possible outbreak scenarios by tracking influxes in cases, sources of exposure, and how the pathogen may have spread (CDC, 2019-b). Both diseases are most often known to be transmitted through fecal matter via the fecal-oral route (CDC, 2019-a). In the context of water, they spread by infected individuals depositing oocytes through their feces that are then consumed in some manner (WHO, n.d.). Examples of this include consumption of untreated sewage-contaminated waters, water that has been treated only by disinfection, failed or inadequate filtration, and damage to water distribution systems (WHO, n.d.). Once transmitted, individual incubation time and symptoms may vary slightly.

Giardiasis is a diarrheal disease brought on by exposure to the microscopic parasite

Giardia duodenalis, also commonly referred to by its outdated taxonomic names G. intestinalis

11 or G. lamblia (CDC, 2019-c). After approximately 6 to 15 days after first exposure, symptoms may include diarrhea, abdominal discomfort, nausea, vomiting, and dehydration (CDC, 2019-d; WHO, n.d.). However, 16-86% of infected individuals may be asymptomatic (WHO, n.d.). This can make surveillance of this notifiable disease particularly difficult as severity varies considerably and not all infected individuals may receive medical care.

Cryptosporidium, sometimes colloquially referred to as “crypto”, is the most commonly seen protozoan parasite that exists in developed countries (Lemery & Auerbach, 2017). A recent study showed that approximately 60% of global protozoan outbreaks between the years

2004 and 2010 could be attributed to Cryptosporidium with nearly all cases coming from areas with developed water facilities in North America, Europe, or Australia (Shirley et al., 2012). Average incubation time for cryptosporidiosis is approximately seven days after first exposure to the parasite (WHO, n.d.). Symptomatic cases of cryptosporidiosis are trademarked by extremely watery diarrhea, stomach cramping, dehydration, nausea, vomiting, and fevers; in other instances, individuals may not show any symptoms at all (CDC, 2019-e). Like giardiasis, infections in the general population typically last less than a week and can often be asymptomatic, making surveillance of this parasite difficult as well. Cryptosporidium is also unique in that it is easily able to evade and resist chlorination, a main strategy used to disinfect water in the United States (Lemery & Auerbach, 2017). When examining the consequences of Giardia and Cryptosporidium, it is important to consider the potential amplified effects to communities with fewer resources, degraded or antiquated infrastructure, or less financial means to handle unexpected catastrophes. While no group is immune to natural disasters, low-income and minority communities are disproportionately vulnerable to the economic hardships of recovery that come after them

(Reeves, 2017). Consistent hurricane damage over many years with a lack of adequate

12 recovery efforts thwarts attempts to maintain a community's ability to stay safe, healthy, and protected. After Hurricane Ike in 2008, thousands of low-income residents in Galveston, Texas were left without clean water and a severely compromised sewage system, leaving them isolated in a community that smelled of feces and stagnant water (Urbina, 2008). In the case of severe hurricanes such as Ike, recovery efforts need to consider the communities most at risk and the water infrastructure present to support disasters in order to prevent a decline in human health (Deaton, 2017).

Goal and Objectives

The overall goal of this thesis was to determine whether post-hurricane infrastructure challenges are associated with increased waterborne disease. We analyzed trends in the geographical impact of recent hurricanes, rates of Giardiasis and Cryptosporidiosis in impacted states, and effects of hurricanes on local water infrastructure, with the expectation that disease rates would be higher in locations suffering hurricane-induced infrastructure damage. This study aim was supported by the following activities:

(1) Evaluate the demographic composition of counties in need of financial assistance in the

post-hurricane period

(2) Collect CDC data on infection rates of Giardia and Cryptosporidium in the states of interest during the time period

(3) Classify and analyze publicly available reports documenting water infrastructure damage

in locations negatively impacted by hurricanes

(4) Examine the temporal association of hurricane occurrence and Giardia and Cryptosporidium infection rates in states impacted by storms (5) Examine the association of Giardia and Cryptosporidium infection rates to documented water infrastructure failures

Methods

The scope of this investigation spans an eighteen-year period (2000-2018) of hurricane and disease statistics in the United States. All climatic and epidemiological events within this time period were examined for consideration in this study.

Four broad categories of data were chosen to represent hurricane activity, demographics, waterborne disease rates, and infrastructure. Data collection in this study was done using a mixed-method approach, utilizing both readily-available quantitative data and qualitative research. Quantitative data was obtained in these areas: (1) geographical hurricane attributes, (2) demographic characteristics of populations struck by aforementioned hurricanes, and (3) CDC confirmed counts of cases of giardiasis and cryptosporidiosis. Qualitative data was collected through random sampling of local and regional news outlets regarding infrastructure of areas affected by specific hurricanes. All quantitative data was received from government agency [NCEI, FEMA, ACS, CDC] surveillance records.

Hurricanes

The National Centers for Environmental Information (NCEI), as a subdivision of the

National Oceanic and Atmospheric Administration (NOAA), collects data on all billion-dollar weather and climate disasters. Tropical cyclones have historically produced the most damage, cumulatively nearly $950 billion since 1980 (NCEI, 2020). The costs associated with these storms was primarily due to their significant infrastructure damage (Insurance Information Institute, n.d.). These storms were appropriate for our sample population, as we are interested in infrastructure damage related to hurricanes that could impact water quality. To establish a more attainable and relevant sample size, the hurricanes were restricted to just those that

15 occurred between the years 2000 and 2018, which resulted in 24 storms. The named storms used in this study in order of the years they occurred are as follows:

Alison (2001) Katrina (2005) Isaac (2012) Lili (2002) Rita (2005) Sandy (2012) Isabel (2003) Wilma (2005) Matthew (2016) Charley (2004) Dolly (2008) Harvey (2017) Frances (2004) Gustav (2008) Irma (2017) Ivan (2004) Ike (2008) Maria (2017) Jeanne (2004) Irene (2011) Florence (2018) Dennis (2005) Lee (2011) Michael (2018)

Once the sample was established, the hurricanes were compared with Federal

Emergency Management Agency (FEMA) financial assistance designation maps provided on the FEMA website. For each storm, a map for each affected state that received government financial aid was included. Each affected county as well as what type of aid received by that county, if any, was present on the map. For each map, there were up to five categories that detailed the type of assistance: (1) No Designation, (2) Public Assistance, (3) Public Assistance

(Categories A and B), (4) Individual Assistance and Public Assistance (Categories A-G), and (5)

Public Assistance (Categories A-G). Public Assistance Categories A and B denote a need for emergency work such as debris removal and protective measures, whereas Categories C through G indicate the need for permanent work such as rehabilitation of roads, bridges, water control facilities, and public utilities (FEMA, 2017). Counties that received the highest amount of aid, Individual Assistance and Public Assistance (Categories A-G), were included in this study.

Each county that received aid had previously declared a disaster within the county as well

(FEMA, 2020).

FEMA maps were not produced for these storms: Alison, Lili, Isabel, Charley, Katrina,

Rita, Lee and Maria. This is because they fell outside of the boundaries for typical FEMA assistance analyses, with Katrina and Maria being too large to document adequately in this format, and the others being too small in the scope of their respective damage. As a result of these storms not being included in standard FEMA maps, the storms listed above were excluded from the analysis that follows in this study.

The following thirteen states were impacted by one or more of the named hurricanes and had FEMA aid maps available: Alabama, Connecticut, Florida, Georgia, Louisiana, Mississippi,

New Jersey, New York, North Carolina, Pennsylvania, South Carolina, Texas, and Vermont.

Demographics

To parameterize the affected population of each storm, census data was retrieved from the American Community Survey (ACS) 2017 estimate and Social Vulnerability Index (SVI) records from the Centers for Disease Control and Prevention (CDC).

ACS census estimates provide insight into county-specific patterns and trends in population. As its name denotes, it is a statistically educated estimate based off of the most recent decadal census and yearly data collected since then. While these estimates are relatively accurate, there may be instances where demographics do not comprehensively reflect actual data from a given year. From the estimated data available, we utilized information regarding each county’s population, mean and median income by household, and race percentages. This information was obtained to compare with aid measures and infrastructural impacts. The most recent data estimating census numbers for 2017 was used and generalized to the entire sample population.

The CDC’s Social Vulnerability Index is defined as “a community’s ability to prevent human suffering and financial loss in a disaster” (CDC, 2018). It is a measure that combines four indicators of community vulnerability: socioeconomic status, household composition and disability, minority status and language, and housing and transportation. Each indicator is split into two to five more groups and categorized further. In this study, we utilized the overall SVI for each county affected. Each SVI is measured on a continuous scale from 0 (lowest vulnerability) to 1 (highest vulnerability) (CDC, 2018). ACS census estimates for each county were recorded, as well.

Diseases

The CDC currently lists twelve waterborne and water-related diseases as nationally notifiable. Because notifiable diseases are required by law to be reported, these were chosen as the diseases of interest as likely to have the most accurate and up-to-date data available. As previously mentioned, the two most closely related illnesses to this study are cryptosporidiosis and giardiasis from the protozoa parasites Cryptosporidium and Giardia lamblia, respectively. Both parasites often result from sewage contamination of drinking and recreational water, making an increased rate of these illnesses very plausible during times of hurricanes and flooding (Lemery & Auerbach, 2017). CDC-published Mortality and Morbidity Weekly Reports (MMWR) during recent decades have assessed and collected annual data on cryptosporidiosis and giardiasis cases throughout each state. Each yearly report includes each U.S. state’s case count, percentage, and rate of cases, as well as CDC-defined outbreak-associated cases. This data was recorded for each of the thirteen states that are part of this study for the years 2000 through 2018. All statistics on disease represent state level data, as county level data is not publicly available.

We also utilized available CDC annual summary reports for cryptosporidiosis and giardiasis for the years 2013 through 2018. This five years of data filled in any gaps of time that

MMWR publications did not cover.

Infrastructure

Publicly available reports and news were used to determine the state of local infrastructure systems for the thirteen states of interest. This was primarily to gain a deeper understanding of the communities’ vulnerabilities from a first hand perspective. It allowed for a broadened collection of trends in opinions of local infrastructure’s strengths and limitations. We utilized varying national, regional, and local news outlet reports and publications and documented specific key-words in each.

Google, a search engine, was used to find reports and publications. Each storm name with select key phrases following it were searched for all thirteen hurricanes. Key phrases included “...infrastructure damage,” “...water pump failure(s),” “...sewage,” “...waterborne disease,” and “...disease.” Articles of interest would be selected and read in their entirety to screen for topics related to infrastructure damage or waterborne disease. For each storm, seven to ten reports or publications were found. This range provided for a solid understanding of the aftermath of each storm and the damage that occurred.

We classified these reports and publications in a table along 12 attributes:

Attribute Name Data Description Type

Source/Outlet String Origin of data (Ex. New York Times and Palm Beach Post)

Associated Hurricane Name String Given name of hurricane

Health hazards Boolean Yes: Discuss any mention of hurricane-related health warnings, disease, known cases and deaths

Infrastructure Boolean Yes: Discuss crucial compromised infrastructure component of lack thereof

Infrastructure assessments Boolean Yes: Discuss assessment of infrastructure after hurricane period done by government agency

Compromised water Boolean Yes: Discuss a compromised feature of water sanitation system infrastructure that is necessary to the system’s function

Compromised water String Direct quotation noting what was compromised sanitation system quote

Compromised drinking Boolean Yes: Discuss a compromised feature of water water infrastructure that impacted public drinking water

Compromised drinking String Direct quotation noting what was compromised water quote

FEMA declaration Boolean Yes: Mention of a FEMA disaster declaration in response to the hurricane and/or damage caused by the hurricane

Type of FEMA declaration String If “FEMA disaster declaration” is a “Yes”, the

disaster type or code is listed here if available

Mention of hurricane related Boolean Yes: Discuss the possibility of an illness or disease disease due to the hurricane and hurricane-related risks

Mention of hurricane related String Direct quotation noting what risks are present disease quote

Cases of hurricane related Boolean Yes: Discuss confirmed cases of disease hurricane-associated disease or illness

Cases of hurricane related String Direct quotation noting what disease or illnesses disease quote are present and how many have occurred

Disease-related fatalities Boolean Yes: Discuss confirmed deaths caused by hurricane-associated disease or illness

Disease-related fatalities String Direct quotation noting what disease or illnesses quote are present and how many have occurred

Notes/Description String Allows for further elaboration of attributes denoted with a “Yes” or relevant information not covered by previous attributes

Link to source String URL source

Table 2. Attribute categories for organization of infrastructure data.

Analysis

Before an analysis could be conducted, all data needed to be grouped in order to match the lowest level of granularity available in the dataset. In this case, all data must be considered at a state level, as disease data is not publicly available at a county level and infrastructure data was largely generalized to include the just state the report came from.

Demographic data was the only variable that required a broadening into a state scale.

For each of the thirteen states, data from all affected counties were averaged together with population sizes considered to create a weighted average. This method produced weighted averages for each state’s income, SVI, and race percentages based upon county populations.

State-level annual disease data was collected for all 16 hurricanes and generated to produce year by year trend line graphs organized by storm. Each graph displayed influxes in yearly case counts from the years 2003 through 2018 to better visualize trends over time. A total of 15 graphs were created to see trends in giardiasis, and 16 were created for cryptosporidiosis.

Hurricane Dolly, which struck Texas in 2008, was not included in this analysis as Texas opted to not report their giardiasis statistics that year. Hurricanes Michael and Florence, both occurring in

2018, were also excluded from further analysis as no data from 2019 was available to be able to see trends in the post-hurricane period. All graphs can be seen in Appendix B.

Results

Hurricanes

Collectively, the 16 storms identified affected 13 states and 444 counties between the years 2000 and 2018. The majority of counties (250, 56%) were affected by a single hurricane in the time period; 136 (30.6%) were affected by two hurricanes, 48 (10.8%) by three hurricanes, and 10 (2.3%) by four hurricanes. Thirty-six FEMA financial assistance designation map codes were retrieved and can be seen in full in Appendix A.

Figure 1. Percentage of FEMA-identified United States counties hit by one or more hurricanes

between 2000 and 2018.

Demographics

In Tables 2 and 3 below, N represents the number of counties in each state that contributed to the averaged results for each of the thirteen states as denoted by the FEMA financial assistance designation maps. All results relating to income, race, and SVI are an average of all available county data weighted by each county's population to create a weighted average.

State N Population Income SVI

Alabama 49 74143 $64,078 0.65

Connecticut 4 561084 $114,235 0.43

Florida 67 302663 $72,956 0.63

Georgia 49 92218 $83,891 0.55

Louisiana 53 76757 $67,868 0.68

Mississippi 26 51245 $57,176 0.80

New Jersey 21 426674 $105,882 0.45

New York 28 537398 $100,462 0.57

North Carolina 46 89515 $70,002 0.64

Pennsylvania 10 370439 $100,280 0.28

South Carolina 24 86004 $66,325 0.65

Texas 55 183861 $81,361 0.70

Vermont 12 50957 $75,886 0.20

Table 3. Weighted state averages of income and SVI based on county population. N is equal to the number of counties that contributed to the state’s average.

States showed an average income ranging between approximately $57,000 to $114,000 per household in Mississippi and Connecticut, respectively. Social vulnerability indices (SVI)

ranged from 0.20 in Vermont and 0.80 in Mississippi. This measure indicates that counties in

Mississippi are generally at an increased vulnerability for negative side effects of natural

disasters and hazard mitigation based upon their available resources for planning and recovery

efforts.

Native American/ Native African Hispanic/ Alaskan Hawaiian/ State N White (%) American (%) Latino (%) Asian (%) Native (%) Islander (%) Other (%)

Alabama 49 67.37 29.84 3.97 1.68 1.19 0.09 1.65

Connecticut 4 78.35 12.41 16.26 5.24 0.97 0.17 6.22

Florida 67 77.79 17.42 24.75 3.39 0.83 0.19 3.12

Georgia 49 58.41 35.40 8.18 5.08 1.00 0.17 2.63

Louisiana 53 64.92 32.22 5.21 2.15 1.29 0.09 1.43

Mississippi 26 59.57 38.86 3.11 1.47 0.69 0.07 0.87

New Jersey 21 69.92 14.77 19.69 10.26 0.67 0.13 7.02

New York 28 59.60 19.51 23.24 11.12 1.01 0.17 12.06

North Carolina 46 66.96 26.49 9.19 3.19 3.00 0.18 3.38

Pennsylvania 10 83.84 10.12 8.26 5.34 0.61 0.10 2.51

South Carolina 24 64.45 33.14 5.10 1.70 1.01 0.10 1.69

Texas 55 73.52 15.04 42.40 5.90 0.95 0.14 6.67

Vermont 12 96.33 1.79 1.80 2.07 1.28 0.12 0.43

Table 4. Weighted state averages of race based on county population. N is equal to the number of

counties that contributed to the state’s average.

Total percentages may not equal 100% due to double-count of some populations.

All states showed a majority (over 50%) of their populations were white. Northeastern

most states show an upward trend to be a higher percentage of white, excluding New York.

Southeastern states tended to be comprised of mostly white individuals, though some states have high percentages of African American populations, such as Mississippi with 38.86%, or high percentages of Hispanic/Latino populations, such as Texas with 42.40%.

In cross-analysis of hurricane and demographic data, I observed no clear trend in the likelihood of multiple storm strikes by household income or SVI. Counties struck three or four times presented slightly higher proportions of white residents (approximately 8%) compared to counties struck 1 or 2 times (approximately 70%), but this observation can likely be associated with a large number of counties presenting as majority white. No strong association was observed between chance of being struck by a hurricane and income, SVI, or race.

Diseases

State-level disease data was collected on an annual basis for all 16 hurricanes via the

CDC. Data was analyzed by storm, with each state’s yearly counts displayed from 2003 through

2018 to visualize trends over time. Hurricane Dolly, which struck Texas in 2008, was not included in this analysis as Texas opted to not report their giardiasis statistics that year.

Hurricanes Michael and Florence, both occurring in 2018, were also excluded from further analysis as no data from 2019 was available to be able to see trends in the after-hurricane period.

Trends seen in giardiasis over time were largely downward. 8 (62%) of the 13 hurricanes showed downticks, or decrease in disease counts, in the year during and after the hurricane occurred, while 2 (15%) showed a positive uptick in cases, and 3 (23%) showed no or very little change at all. No case counts in post-hurricane periods were large in their changes and were mostly slight upticks or downticks. Strictly downward trends can be seen in Hurricanes Irma,

Matthew, Sandy, Isaac, Irene, Wilma, Dennis, and Frances. Slight upticks were seen only after

Hurricanes Ike and Gustav. Ike and Gustav occurred in the same year, so it is difficult to tell if one or both storms contributed to the uptick in disease, or if this was due to other causes.

Figure 2. Total giardiasis cases per state for Hurricane Sandy. The black diamond

represents the year that the storm occurred.

Figures 3. Total giardiasis cases per state for Hurricane Ike. The black diamond

represents the year that the storm occurred.

Cryptosporidiosis trends over time were primarily upward. 8 (57%) of the 14 hurricanes showed upticks in cases in the year during and after the hurricane occurred, while 3 (21%) showed a downtick and 3 (21%) showed no or very little change at all. Some graphs portrayed very large upticks in the year-long time-period following a hurricane, such as with Hurricanes Ike and Dolly in 2008. Both hurricanes occurred in the same year, so it is difficult to tell if this uptick can be attributed to either of these hurricanes or to outside causes.

Figure 4. Total cryptosporidiosis cases per state for Hurricane Ike. The black diamond represents the year that the storm occurred.

Figure 5. Total cryptosporidiosis cases per state for Hurricane Dolly. The black diamond represents the year that the storm occurred.

Based upon the case counts over time for both giardiasis and cryptosporidiosis, no clear trends can be observed and a more sophisticated approach may be necessary to identify upticks at an increased granularity.

Infrastructure

A total of 122 reports were collected, each associated with one of the sixteen storms of interest. Each storm had between seven and ten reports specific to the events of that named hurricane. Of the 122 total reports, 99 (81%) regarded infrastructure, 31 (25%) regarded health hazards, and 4 (3%) regarded government-performed infrastructure assessments. 12 reports fell into both the infrastructure and health hazards category.

Figure 6. Percentage of reports in each of the three categories of infrastructure data.

Reports about infrastructure made up the bulk of the collected data, as it was most often a topic of discussion after a water-related weather event. 73% (72) of reports about infrastructure included documentation of a compromised water sanitation system feature in that

30 location. Examples of compromised features in direct quotes from the text include: “floodwaters overtaxed lift stations and flooded septic tank drain fields,” “pump station failure in Stoney Creek

Plantation,” “knocked out the systems main input pump,” and “71 million gallons of wastewater spilled” (Bonner, 2019; KBMT, 2018; Waymer, 2018; WECT, 2018). 17% (17) of infrastructure reports also mentioned compromised drinking water sources, with 11 specifically noting boil-water notices to residents of the local area.

Of the 31 reports that discussed possible health hazards related to a specific named hurricane, all 31 of them mentioned the potential for an increased risk of hurricane related disease, while only 2 (6%) included actual confirmed cases of hurricane related disease. Of the

2 confirmed cases, one of them was gastrointestinal related but it was unclear exactly which illness that individual suffered from at the time, though their symptoms align with a waterborne infection. 2 reports also discussed confirmed deaths caused by hurricane related disease, though neither of them were directly related to waterborne illnesses. Examples of hurricane related disease mentioned in the text include: “cholera, Hepatitis A, and vibriosis,” “spike in waterborne illness,” “increase in our reported cases of gastro illnesses,” “raw sewage can result in salmonella poisoning or giardia,” and “mold growth in homes linked to upper respiratory tract symptoms, cough, and wheeze” (AP, 2018-b; Hughes, 2018; Manuel, 2013; Murawski, 2018). Government-performed infrastructure assessments were the least seen type of report.

Two assessments were in affiliation with NOAA, one was by FEMA, and the other by the U.S.

Department of Defense. None of the reports described substantial water-related damage, with some mentioning that “there were no mechanical malfunctions,” or that previous city projects prevented further damage to infrastructure once the hurricane came (NOAA, 2017; Parsons, 2009).

FEMA-related financial assistance or declarations were mentioned a total of three times in all reports. There was one official declaration number provided in a report about Hurricane

Dennis that was linked to the FEMA financial assistance designation map for Florida in 2005

(Disaster Center, 2005). This map’s code can be referenced in Appendix A. The remaining two reports did not include specific documentation of FEMA declaration or assistance, but did mention that financial assistance was provided at some point after the hurricane struck. These two reports were affiliated with Hurricanes Florence and Dolly. A full collection of sources used in this analysis can be found in Appendix C.

Hurricane Name Observed Giardiasis Trend Observed Cryptosporidiosis Trend

Frances ↓ Downward ↑ Upward

Ivan → Neutral → Neutral

Jeanne → Neutral → Neutral

Dennis ↓ Downward ↑ Upward

Wilma ↓ Downward ↑ Upward

Dolly N/A ↑ Upward

Gustav ↗ Slightly upward → Neutral

Ike ↗ Slightly upward ↑ Upward

Irene ↓ Downward ↓ Downward

Isaac ↘ Slightly downward ↑ Upward

Sandy ↘ Slightly downward ↘ Slightly downward

Matthew ↓ Downward → Neutral

Harvey → Neutral ↑ Upward

Irma ↓ Downward ↘ Slightly downward

Florence N/A N/A

Michael N/A N/A Table 5. Observed trends in giardiasis and cryptosporidiosis cases after each hurricane.

No data is available for giardiasis trends for Hurricane Dolly due to missing reporting from Texas.

No data is available for giardiasis and cryptosporidiosis trends for 2018 Hurricanes Florence and Michael because 2019 data is not available for comparison.

In a cross-analysis of infrastructure and disease, only 1 (7%) storm resulted in both significant documented damage to water infrastructure systems and flooding and upticks in both giardiasis and cryptosporidiosis in the year following the hurricane. 50% (7) of storms were observed to have both large-scale damage and crippled infrastructure while also sustaining an

33 uptick in either giardiasis or cryptosporidiosis, but not both waterborne diseases. A total of 6 of these 7 storms had a loose association with increases in exclusively cryptosporidiosis and infrastructure damage at the same time. 6 (43%) storms had a negative association with an increased level of infrastructural damage, but a decrease in overall disease counts. From current data, it is not feasible to determine an association between rates in waterborne disease and infrastructure damage after hurricanes without a full statistical analysis and a more granular level of data.

Discussion, Limitations, and Conclusions

Discussion

While no statistical analyses were completed in this study, all results were interpreted through a lens of observation and trend-seeking. Under this premise, no causal relationships can be determined but this research sets the groundwork for future research in this area. No conclusive evidence was found that confirmed the initial hypothesis: that for areas struck by hurricanes in the last twenty years, there was: 1) a temporal association between hurricane occurrence and giardiasis and cryptosporidiosis infection rates, and 2) an association of infection rates to water infrastructure failures. Our results did not show an association of giardiasis and infrastructure failures, but a potential relationship of cryptosporidiosis and infrastructure failures. While this result fails to confirm the initial hypothesis, the available data were likely not sufficient to make strong claims one way or another about these topics and their interrelationships.

A major obstacle in conducting this study was the lack of granularity and specificity in available data sources. Publicly available disease counts for giardiasis and cryptosporidiosis do not exist on a monthly level. Therefore, analyses of association to disease are unable to provide sufficient evidence of a positive or negative causal relationship. Study activities outlined earlier in this paper were largely achieved, particularly those relating to data collection (1-3), but did not support the stated hypothesis.

Trends seen among disease rates and infrastructure failures indicate that additional variables are not being considered in this analysis. It is possible that outside factors contributed to influxes in rates of disease and infrastructure failures, either together or individually, but these were not included within the scope of this study. For example, cryptosporidiosis cases show an

35 annual uptick in the majority (57%) of hurricane years studied. Because these upticks may be attributed to outside factors or point events, such as summertime recreational swimming incidents or the sheer volume of pathogens present when compared to giardiasis, only a loose positive association can be made. Without further evidence, causal relationships cannot be confirmed.

A general trend of decreased giardiasis cases in years following a major hurricane was an unexpected and unexplained finding. Little to no research was found concerning the relationship between giardiasis and hurricanes in the initial literature review, though some studies did report a link between rates of giardiasis and contaminated water sources in general

(Halliez & Buret, 2013). However, those reports primarily reported on developing countries that did not have comparable water infrastructure systems to most regions in the United States.

Additionally, with the low rates of symptomatic infected individuals that may seek medical care,

CDC-given rates of giardiasis that were used in this analysis likely do not reflect actual case counts. With this information, it seems probable that the decrease in giardiasis rates may have no or very little association to the presence of hurricanes, but more of a connection to contaminated water sources through other means. This decrease in giardiasis rates could also be attributed to the number of infected individuals that may not seek medical care in the days or weeks following a hurricane due to a lack of resources to do so.

Limitations

While this study remained within its intended scope, there were limitations with the format and delivery. There is a need to refine and amplify the degree to which each component is investigated while also considering outside factors that may contribute to study results. The

36 data collected lacked both spatial and temporal precision, which affects ability to observe associations that are inherently location- and time-dependent.

A leading limitation to this study is the spatial scale at which current data is given.

Ideally, disease counts would be available and summarized at a county level to match demographic data measurements. To be comparable, infrastructure data would have been categorized by the county in which the failures occurred, allowing the overall analysis to take place on a county, rather than generalized state level. By creating state averages from county level data, it is likely that some data is not being accurately represented and any outliers are being lost in the analysis. Namely, I am unable to see isolated counties that may reflect much more severe increased disease rates or infrastructure damage.

Temporal limitations are present within the data as well. Disease data could have been further analyzed on a similar level to demographic and infrastructural data if it were provided on a monthly or weekly level. Ideally, disease data would include a precise date of infection.

Because the time-frame in which an outbreak may occur after a storm, it is necessary to have the ability to see day-to-day or week-to-week changes in disease counts to fully understand the extent of disease. As stated previously, a symptomatic individual will begin exhibiting symptoms approximately 6 to 15 days after first exposure, making the first two to three weeks after a hurricane a crucial time period for disease tracking (WHO, n.d.). Additionally, demographic data would have been annual statistics that also measured the movement of people over time, rather than a cross-section of individuals in 2017. While demographic data contained educated estimates, no consideration was taken for changes in the population within the past twenty years. Had demographic data contained accurate annual statistics that included influxes in county populations, analyses reflecting trends between income, SVI, and disease would have

37 been more accurate. The relationship between county-level income and disease trends over time could have been further investigated as well.

Limitations to qualitative data collection vary. Qualitative data retrieval is a labor- and time-intensive process that provides additional obstacles in statistical analysis. Bias was present in key-words and phrases selected and assigned to data points, but were minimized as much as possible by verifying results with concurrent reports and publications. The possibility of reporting bias is also present and cannot be fully eliminated, thus having the potential to influence what sources are reported based upon what infrastructure failures may be present in them. This can potentially limit the ability to have a full and comprehensive dataset by not including sources that do not mention infrastructure failures or hurricane-related disease. By cross-analyzing qualitative data with quantitative data from other research variables, these limitations were further minimized (Radu, 2019). A lack of higher-level statistical analysis contributes to making this study a preliminary analysis based upon general trends and observations, rather than an in-depth research study that is meant to propose conclusive and exhaustive results that can contribute to solutions to issues discussed. While this limits the advancement that these study results can bring, it does expose areas of research that require further study.

Conclusions

Much research currently exists on water sources and their relationship to the prevalence of protozoan parasitic infections, but there is little documented about the connection water sources and disease have with the water infrastructure itself. In a typical United States city, water is typically distributed from water treatment facilities; it is crucial to consider the resources and capability of the facilities to keep water safe and well-maintained when under significant

38 environmental stress. Future research on the relationships between waterborne disease and infrastructure failures would benefit communities that have unexplained increases in disease or antiquated infrastructure that is not suitable to properly supply households with clean water.

Greater resources and access to data will be a critical part in the capabilities of any further investigations. Possible considerations for future research in this domain are indirect causes of waterborne disease upticks, such as recreational water sources, and available resources for areas with less financial means to combat weak infrastructure which could prevent future failures. Examples of more scenarios can be seen throughout the qualitative data sources collected in Appendix C.

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Appendix A - FEMA Financial Assistance Designation Maps (FEMA-DR)

Each FEMA-DR code corresponds with state maps for each hurricane’s financial assistance designation appointments.

Frances (2004) Gustav (2008) ● Georgia: FEMA-1560-DR ● Louisiana: FEMA-1786-DR ● Florida: FEMA-1545-DR Ike (2008) Ivan (2004) ● Louisiana: FEMA-1792-DR ● Alabama: FEMA-1549-DR ● Texas: FEMA-1791-DR ● Florida: FEMA-1551-DR ● Georgia: FEMA-1554-DR Irene (2011) ● Mississippi: FEMA-1550-DR ● New Jersey: FEMA-4021-DR ● New York: FEMA-4020-DR Jeanne (2004) ● Pennsylvania: FEMA-4025-DR ● Florida: FEMA-1561-DR ● Vermont: FEMA-4022-DR

Dennis (2005) Isaac (2012) ● Alabama: FEMA-1593-DR ● Louisiana: FEMA-4080-DR ● Florida: FEMA-1595-DR ● Mississippi: FEMA-4081-DR ● Mississippi: FEMA-1594-DR Sandy (2012) Wilma (2005) ● Connecticut: FEMA-4087-DR ● Florida: FEMA-1609-DR ● New Jersey: FEMA-4086-DR ● New York: FEMA-4085-DR Dolly (2008) ● Pennsylvania: FEMA-4099-DR ● Texas: FEMA-1780-DR

Matthew (2016) Irma (2017) ● Georgia: FEMA-4284-DR ● Florida: FEMA-4337-DR ● North Carolina: FEMA-4285-DR ● Georgia: FEMA-4338-DR ● South Carolina: FEMA-4286-DR Florence (2018) Harvey (2017) ● North Carolina: FEMA-4393-DR ● Louisiana: FEMA-4345-DR ● South Carolina: FEMA-4394-DR ● Texas: FEMA-4332-DR Michael (2018) ● Florida: FEMA-4399-DR ● Georgia: FEMA-4400-DR

Appendix B - Waterborne Disease Trend Graphs Overview of yearly trends of giardiasis and cryptosporidiosis cases before and after each storm. The black diamond on each graph represents the year the storm occurred.

Appendix C - Infrastructure Sources Each source used during infrastructure data collection is hyperlinked and listed per storm.

Hurricane Frances (2004) ● https://www.wwdmag.com/channel/casestudies/storm-proof-sewers ● https://www.foresternetwork.com/home/article/13003325/hurricanes-the-effects-on-stormwater-m anagement ● https://www.fema.gov/media-library-data/20130726-1712-25045-1511/hurricane_frances___jean ne_s_impact_on_rural_wastewater_systems.txt ● https://www.nhc.noaa.gov/data/tcr/AL062004_Frances.pdf ● http://www.nbcnews.com/id/5928764/ns/weather-weather_news/t/life-after-frances-florida-reboun ds-slowly/#.XmbEopNKjOQ ● https://www.waterworld.com/home/article/16224672/pumps-help-cleanup-of-florida-sites-damage d-by-hurricane-frances ● https://www.citizen-times.com/story/news/local/2014/09/06/hurricanes-frances-ivan-impact-lingers -years-later/15217637/

Hurricane Ivan (2004) ● http://fwrj.com/techarticles/1009%20Tech1.pdf ● https://www.wuwf.org/post/hurricane-ivan-10-years-later-cleaning-and-moving-forward#stream/0 ● http://www.hurricanescience.org/history/storms/2000s/hurricaneivan/ ● http://www.nbcnews.com/id/5960400/ns/weather-weather_news/t/hurricane-floods-give-way-filth- mud/#.XmbJzJNKjOQ ● https://www.starnewsonline.com/news/20050710/ivan-survivor-refuses-to-evacuate-her-home ● https://www.weather.gov/mob/ivan ● https://www.wwdmag.com/ecua-central-wastewater-treatment-plant-project

Hurricane Jeanne (2004) ● https://www.nrc.gov/docs/ML0533/ML053340263.pdf ● https://www.wwdmag.com/channel/casestudies/storm-proof-sewers ● https://www.wwdmag.com/channel/casestudies/storm-proof-sewers

● https://www.fema.gov/media-library-data/20130726-1712-25045-1511/hurricane_frances___jean ne_s_impact_on_rural_wastewater_systems.txt ● https://www.nytimes.com/2004/09/27/us/another-hurricane-roars-across-midflorida.html ● http://www.hurricanescience.org/history/storms/2000s/jeanne/ ● https://www.roanoke.com/archive/raw-sewage-spill-was-massive-reports-say/article_5db9a8a7-b 4ef-51c0-a480-d901bb944e19.html

Hurricane Dennis (2005) ● https://www.fema.gov/news-release/2006/07/07/monday-marks-hurricane-dennis-anniversary ● https://floridadep.gov/sites/default/files/HurricaneDennis-HurricaneKatrina.pdf ● https://www.cs.mcgill.ca/~rwest/wikispeedia/wpcd/wp/h/Hurricane_Dennis.htm ● http://www.disastercenter.com/Tropical%20Storm%20-%20Hurricane%20-%20Dennis.htm ● https://www.theguardian.com/environment/2005/jul/11/usnews.naturaldisasters ● https://www.gainesville.com/news/20050715/hurricane-dennis-surge-causes-serious-headaches ● https://www.weather.gov/mob/dennis

Hurricane Wilma (2005) ● https://www.miamiherald.com/news/state/florida/article171543712.html ● http://www.disastercenter.com/Tropical%20Storm%20-%20Hurricane%20-%20Wilma.html ● http://www.disastercenter.com/Tropical%20Storm%20-%20Hurricane%20-%20Wilma.html ● http://miamidade.floridahealth.gov/programs-and-services/infectious-disease-services/disease-co ntrol/_documents/2005-epi-nov.pdf ● https://www.wlrn.org/post/south-florida-faces-costly-sewage-problem#stream/0 ● https://www.theguardian.com/environment/2005/oct/24/usnews.hurricanes2005 ● http://www.hurricanescience.org/history/storms/2000s/wilma/

Hurricane Dolly (2008) ● http://insectsinthecity.blogspot.com/2008/08/demands-for-mosquito-control-likely-to.html ● https://www.chron.com/news/hurricanes/article/South-Texas-begins-cleanup-after-Hurricane-Doll y-1613894.php ● https://www.tomudall.senate.gov/news/press-releases/udall-heinrich-pearce-announce-24-million- for-hurricane-dolly-recovery-in-ruidoso

● https://www.reuters.com/article/us-storm-dolly/hurricane-dolly-hits-south-texas-flooding-feared-id USN2227871720080723 ● https://www.nytimes.com/2008/07/24/us/24hurricane.html ● https://www.weather.gov/bro/2008event_dollyreport

Hurricane Gustav (2008) ● https://www.reuters.com/article/us-storm-gustav/new-orleans-mops-up-after-gustav-close-encoun ter-idUSN2541891320080902 ● https://www.reuters.com/article/us-storm-gustav/new-orleans-mops-up-after-gustav-close-encoun ter-idUSN2541891320080902 ● https://www.foxnews.com/wires/2008Sep01/0,4670,GustavGulfCoast,00.html ● https://www.wateronline.com/doc/patterson-pumps-meet-hurricane-gustav-0001 ● https://media.defense.gov/2009/Feb/27/2001712341/-1/-1/1/ParsonsRep022709.pdf ● https://www.houmatoday.com/article/DA/20080901/News/608093112/HC ● https://abcnews.go.com/US/Weather/story?id=5701188&page=1

Hurricane Ike (2008) ● https://grist.org/article/ike2/ ● https://www.khou.com/article/news/galveston-streets-sinking-16-months-after-ike/342436178 ● https://www.balfourbeattyus.com/our-work/project-portfolio/galveston-wastewater-treatment-plant ● http://www.nbcnews.com/id/27034445/ns/weather-hurricane_ike/t/environmental-damage-widespr ead-after-ike/#.XmBFNTJKjOQ ● https://www.nytimes.com/2008/09/15/us/15galveston.html ● https://www.reuters.com/article/us-storm-ike-galveston/hurricane-ike-shakes-galvestons-economi c-boom-idUSN1726291520080917 ● https://www.houmatoday.com/article/DA/20081012/News/608095244/HC

Hurricane Irene (2011) ● https://cnsmaryland.org/2011/09/02/sewage-spill-after-hurricane-irene-limits-shellfish-harvesting-i n-part-of-chesapeake/ ● https://ocgov.net/oneida/sites/default/files/health/HealthAlerts/Following%20extensive%20floodin g%20in%20some%20areas%20in%20the%20aftermath%20of%20Hurricane%20Irene.pdf

● https://www.washingtonpost.com/national/health-science/irenes-deluge-sends-waste-into-waterw ays/2011/08/29/gIQArcrMoJ_story.html ● https://www.washingtonpost.com/blogs/post_now/post/hurricane-irene-causes-overflows-at-sewa ge-plants/2011/08/29/gIQATlCipJ_blog.html ● https://cbf.typepad.com/bay_daily/2011/08/in-advance-of-tropical-storm-irene-this-weekend-the-m aryland-department-of-the-environment-banned-all-harvesting-of-oysters.html ● https://www.baltimoresun.com/maryland/bs-xpm-2011-09-02-bs-md-sewage-spill-20110902-story .html ● https://abcnews.go.com/Health/hurricane-irene-fallout-trigger-allergies/story?id=14435658

Hurricane Isaac (2012) ● https://www.nola.com/news/environment/article_e3c0a945-cb41-5cbe-b401-d0c063ee4be5.html ● https://www.businessinsider.com/hurricane-isaac-250000-residents-of-new-orleans-without-power -2012-8 ● https://www.waterworld.com/technologies/pumps/article/16193127/new-orleans-assesses-damag e-from-hurricane-isaac ● https://www.nytimes.com/2012/08/30/us/west-nile-deaths-rise-but-isaac-unlikely-to-add-to-peril.ht ml ● https://www.seeker.com/dangers-of-heat-and-disease-follow-isaac-1765947978.html ● https://thelensnola.org/2019/07/17/the-army-corps-of-engineers-has-failed-new-orleans/ ● https://www.dailymail.co.uk/news/article-2195893/Hurricane-Isaac-2012-60-000-ordered-evacuat e-dam-threatens-break-Mississippi-Louisiana-border.html

Hurricane Sandy (2012) ● https://www.villagevoice.com/2013/04/30/hurricane-sandy-unleashed-11-billion-gallons-of-crap-in- the-water-report-shows/ ● https://restorationeze.com/hurricane-sandy-reminds-dangers-raw-sewage/ ● https://www.huffpost.com/entry/hurricane-sandy-sewage-toxic-_n_2046963 ● https://www.nytimes.com/2013/05/01/nyregion/hurricane-sandy-sent-billions-of-gallons-of-sewage -into-waterways.html ● https://medium.com/nycwater/hurricane-sandys-impacts-582f60fe6933 ● https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3673205/ ● https://www.nytimes.com/2012/11/30/nyregion/sewage-flows-after-hurricane-sandy-exposing-flaw s-in-system.html

● https://www.climatecentral.org/news/11-billion-gallons-of-sewage-overflow-from-hurricane-sandy- 15924

Hurricane Matthew (2016) ● https://www.news-journalonline.com/news/20161107/daytona-reports-14m-gallons-of-wastewater -flowed-into-halifax-during-matthew ● https://www.wateronline.com/doc/taking-a-toll-of-hurricane-matthew-s-wastewater-damage-0001 ● https://www.jacksonville.com/news/2016-10-09/jea-reports-seven-sewage-spills-related-hurricane -matthew ● https://www.jacksonville.com/news/2016-10-08/jea-blames-power-outages-sewage-releases-duri ng-hurricane-matthew ● https://www.newsobserver.com/news/politics-government/article236869228.html ● http://www.rmmagazine.com/2016/12/01/hurricane-matthews-destructive-path/ ● https://www.pilotonline.com/news/environment/article_8629e5c9-5be0-5053-bb08-7e14f0871a54. html ● https://www.wavy.com/news/2-norfolk-pump-stations-that-failed-during-hurricane-matthew-to-get- renovations/ ● https://www.news4jax.com/news/2016/10/13/i-team-why-did-jea-lift-stations-leak-sewage-during-s torm/

Hurricane Harvey (2017) ● https://www.sciencedaily.com/releases/2018/08/180801093703.htm ● https://www.nbcnews.com/storyline/hurricane-harvey/hurricane-harvey-may-leave-behind-health- hazards-water-n796791 ● https://www.cbsnews.com/news/houston-water-filtration-plant-harvey-flooding-impact/ ● https://www.cnn.com/2017/08/31/us/harvey-houston-texas-flood/index.html?sr=twCNN083117har vey-houston-texas-flood1124AMVODtop ● https://thehill.com/blogs/blog-briefing-room/348444-boil-water-advisory-issued-for-parts-of-housto n-area ● https://www.statesman.com/NEWS/20170826/Boil-water-notice-issued-in-Victoria-County ● https://www.wwno.org/post/drainage-pump-catches-fire-new-orleans-prepares-harvey ● https://www.panews.com/2017/09/30/despite-harveys-history-rain-dd7s-operations-draw-question s-possible-lawsuit/

● https://www.khou.com/article/weather/hurricane/baptist-hospitals-moving-patients-due-to-failed-w ater-pump/502-469485190

Hurricane Irma (2017) ● https://www.businessinsider.com/hurricane-irma-flooding-sewage-to-flood-streets-homes-and-wat erways-2017-9 ● https://newrepublic.com/article/144798/floridas-poop-nightmare-come-true ● https://www.naplesnews.com/story/news/local/2017/09/15/collier-temporarily-shut-off-drinking-wat er-fix-sewage-pumps/671951001/ ● https://www.washingtonpost.com/news/energy-environment/wp/2017/09/15/in-irmas-wake-million s-of-gallons-of-sewage-and-wastewater-are-bubbling-up-across-florida/ ● https://www.news4jax.com/news/2017/09/14/15m-gallons-of-sewage-spills-as-irma-rains-200b-ga llons-on-jacksonville/ ● https://fortune.com/2017/09/15/hurricane-irma-damage-update-sewage/ ● https://coast.noaa.gov/states/stories/pump-saves-city-from-hurricane-related-flood-damage.html ● https://www.palmbeachpost.com/news/local/after-hurricane-irma-pump-failed-spilling-sewage-delr ay-beach/Ef0m4lfl5wu5ufJD7N2GhL/

Hurricane Florence (2018) ● https://www.nytimes.com/2018/09/19/climate/florence-hog-farms.html?auth=link-dismiss-google1t ap ● https://www.thepilot.com/news/still-reeling-from-florence-robbins-looks-for-ways-to-fix/article_633 c43e4-fe7c-11e8-81c5-07732a8ee718.html ● https://massivesci.com/articles/hurricane-florence-flood-water-contamination/ ● https://www.wect.com/story/39179862/florence-flooding-causes-h2go-pump-failure-228000-gallon s-of-sewage-spilled/ ● https://www.h2goonline.com/Blog/259413/Hurricane-Florence-Causes-Pump-Station-Failure ● https://www.newsobserver.com/news/business/article220561095.html ● https://www.nbcnews.com/news/us-news/danger-may-still-be-lurking-florence-s-floodwaters-even -after-n909711 ● https://apnews.com/7cf07c792fc54d158262903c58ef4833/Florence%27s-water,-not-winds,-will-b e-the-long-term-problem ● https://www.pbs.org/newshour/science/after-hurricanes-why-is-it-so-hard-to-test-for-waterborne-di seases

● https://www.ajc.com/news/world/scary-infectious-illnesses-you-can-catch-from-floodwater/AfZS9V l1iwREMa68nHMTkL/

Hurricane Michael (2018) ● https://weather.com/storms/hurricane/news/2018-10-11-michael-health-hazards-electrocution-infe ction-injury ● https://www.reuters.com/article/us-storm-michael-sewage/sewage-spill-from-hurricane-michael-su spected-in-florida-fish-kills-idUSKCN1MS396 ● https://www.weather.gov/media/tae/events/20181010_Michael/StEER_PVAT.pdf ● https://www.tallahassee.com/story/news/2018/10/12/hurricane-michael-like-bomb-went-off-jackso n-county/1615834002/ ● https://www.fema.gov/news-release/2019/01/25/fema-mitigation-team-assesses-hurricane-micha els-effects-buildings ● https://www.floridatoday.com/story/weather/2018/10/12/hurricane-michael-devastate-environment /1588926002/ ● https://www.npr.org/2019/10/10/768722573/recovery-is-slow-in-the-florida-panhandle-a-year-after -hurricane-michael ● https://www.usatoday.com/story/news/2018/10/11/hurricane-michael-disgusting-water-downed-po wer-lines-pose-danger/1596860002/