The Eco-Epidemiology of Chagas Disease Risk in Panama

THE ECO-EPIDEMIOLOGY OF CHAGAS DISEASE RISK IN PANAMA

ERIN ALLMANN UPDYKE

DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Entomology in the Graduate College of the University of Illinois at Urbana-Champaign, 2018

Urbana, Illinois

Doctoral Committee:

Associate Professor Brian F. Allan, Chair and Director of Research Professor May R. Berenbaum Professor Andrew V. Suarez Assistant Professor James P. O’Dwyer

ABSTRACT

Chagas disease is a vector-borne disease affecting between 5 to10 million people worldwide. It is spread by dozens of species of blood-feeding triatomines (Hemiptera: Reduviidae). Trypanosoma cruzi, a kinetoplastid protozoan parasite and causative agent of Chagas disease, is shed in the feces of triatomines during blood-feeding and must enter the bloodstream through the bite wound or mucous membranes. Intensive studies into the domestic transmission cycle of Chagas disease have elucidated important ecological and epidemiological risk factors for the regions of most intense transmission, particularly the “southern cone” of South America. In contrast, we know little about sylvatic transmission, whereby triatomine species living in natural environments are attracted to the human-built environment. This dissertation examines the factors that attract sylvatic vectors to the domestic environment in Panama, which species of kissing bugs may contribute to disease transmission, and how these factors vary across a human land-use gradient. First, I tested a new collection method that is simpler and more cost- effective than current methods to attract and capture sylvatic triatomines, an important step in monitoring triatomine populations and vector control. This new collection method, which uses a lightweight, inexpensive, cross-vane panel trap and an ultraviolet light, is as effective as traditional light trapping in attracting and capturing triatomines and can be deployed with far less human-power than typical sylvatic triatomine traps. Next, I examined the role of lesser-studied species of sylvatic triatomine in human disease transmission. Over two years of collections across central Panama, I collected 314 triatomines in four genera, Triatoma, Rhodnius, Panstrongylus, and Eratyrus, and tested specimens for infection with T. cruzi. I examined differences in infection prevalence among species, and found that, while R. pallescens was the most abundant triatomine collected throughout the study, P. geniculatus had significantly higher infection prevalence, and R. pallescens may account for only a portion of the total vector potential in this system. Additionally, I performed an eco-epidemiological investigation to examine the risk factors that may contribute to Chagas disease exposure risk across an urban- to-rural land-use gradient in central Panama. In total, 182 houses participated in an epidemiological survey to identify potential risk factors and completed sixteen months of in- home triatomine collections. I determined that land use context as well as the number of domestic animals, especially chickens, are significantly predictive of the density of triatomines as well as the density of infected triatomines collected around the home. Finally, I developed a mathematical model to predict the risk of Chagas disease infection for an individual over time based on parameters from my research including the abundances and T. cruzi infection rates of ii triatomines present around the home, and the factors that attract them to the household environment. This model also can be used to estimate the incidence of Chagas disease infection across Panama in specific land use contexts and generates predictions in line with the World Health Organization’s estimates. Collectively, these studies provide insight into the transmission cycle of Chagas disease in Panama, which can be useful in other contexts where multiple triatomine species co-occur and which will be essential in achieving WHO’s goals of interruption of vectoral transmission of Chagas disease by 2020.

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ACKNOWLEDGMENTS

This dissertation is the final product of five years of my work (and my blood, sweat, and tears, truly), but it would never have been completed had I been alone. There are so many people who helped me along the way. Physically, helping me write (and re-write) and edit (and re-edit) countless grant applications and this here thesis. Mentally, helping to conceptualize this project and see it through. And emotionally, helping guide me through the times—and there were many—that it didn’t seem possible to finish. So, I’d like to take this opportunity to thank these people, and, in the likely event that I forget some, if you are reading this I’d like to thank you, too. Before I thank the many heroes whom I credit with making this dissertation actually happen, I want to thank two people who first sparked my interest in disease and started me down the path that has led me here. Dr. Armand Kuris, who warned me that 10% of his students’ lives were changed by his parasitology class, and then proceeded to change my life forever. Thank you for opening my eyes to the world of parasites. Dr. Al Katz, who was an important mentor during my master’s and without whom I’d never have even thought to apply to MD/PhD programs. Thank you for your advice and support, and your belief that I was capable of not one, but two, doctorates. First, I’d like to thank my advisor, Dr. Brian Allan, who not only has been an excellent research advisor, who not only has wordsmithed probably a thousand pages of my ramblings, but who also, when I told him quite frankly I wasn’t sure if continuing in research was in the cards for me, not only continued to support me as his student but made it clear that no matter my future career he would be supportive. I have been incredibly fortunate to have you as my advisor on this long, frustrating, exciting, overwhelming journey. I’d also like to thank my entire committee for their advice and insight on this dissertation. Dr. May Berenbaum, for making me feel welcome in the department before I’d even arrived, and for offering both critique and support throughout the formulation and writing of this dissertation. Dr. Andy Suarez, for your knowledge of tropical biology, your edits and help in writing, and your positive outlook, which makes the most intimidating parts of academia seem achievable. Dr. James O’Dwyer, for making it possible for me to not only understand (some) mathematical modeling, but also helping me craft a model I am proud of for this dissertation. I’d like to say thank you to Dr. Larry Hanks and his lab, for letting me borrow (and subsequently have American Airlines lose) his cross-vane panel traps and pheromone capture equipment. Thanks also to the Brian Allan Lab, current and past: Dr. Allie Gardner, Erin Welsh, Allison Parker, Elijah Juma, Dr. Page iv

Fredericks, Dr. Andrew MacKay, Tanya Josek, and others who helped edit and hone numerous grants, presentations, and publications. Thanks as well are due to Dr. Owen McMillan, Dr. Ana Spalding, and many others at the Smithsonian Tropical Research Institute, who helped me through many tedious permitting processes that at times seemed endless, but always worked out somehow. Huge thank yous to my three field assistants: Christian Harris, Cami Alfonso Parra, and especially Argelis Aneth Sanchez who worked double days more times than she signed up for, I am sure. I would never have been able to do the work I did without these three individuals and I am forever grateful to them. I’d also like to thank some unofficial field assistants: Salvatore Anzaldo, triatomine finder extraordinaire, and Erin Welsh, Nate Smith, Dr. Ummat Somjee, Rachel Crisp, Austin Garrido, and so many others at STRI who went collecting with me, watched traps for me, and made life in Panama so much better. I also want to thank Dr. Nicole Gottdenker and Dr. Azael Saldaña who showed me the ropes in Panama and introduced me to the Chagas disease system at the very beginning. I’d be remiss not to thank the MSP program, without whom I would not only be penniless, but wouldn’t even be here to begin with. The opportunity to have been working towards my MD while completing this PhD has kept me sane, scheduled, and optimistic about my future. Of course, I need to thank my family, who may have told me I was crazy for moving from Hawaii to central Illinois (and they may not have been entirely wrong, for it takes a certain type of crazy to commit to a program such as this), but who have supported me in everything I do since, quite literally, birth. Thank you to Sam Primer, Rachel Moran, Sara Johnson, Dr. Laura Stein, Dr. Rhiannon Peery, Lisa Mitchem, and Michelle St. John: y’all know I would be lost without you. Thank you for being the best support system I could have ever dreamed of. To Erin Welsh: if ever there were soul mates, I have no doubt you’d be one of mine, and there’s no way in heck I would be finishing this dissertation if it wasn’t for you. And finally, but most importantly, thank you to my partner in all things, who has made these last five years not only bearable but possible, and for whom my gratitude and love cannot find the right words. Brett Updyke, I’m so very lucky to have a criss-cross like you. Thank you.

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION ...... 1

CHAPTER 2: AN EXPERIMENTAL EVALUATION OF CROSS-VANE PANEL TRAPS FOR THE COLLECTION OF SYLVATIC TRIATOMINES (HEMIPTERA: REDUVIIDAE) ...... 13

CHAPTER 3: INVESTIGATION OF TRYPANOSOMA CRUZI INFECTION RATES ACROSS SYLVATIC TRIATOMINE SPECIES IN PANAMA ...... 26

CHAPTER 4: ECO-EPIDEMIOLOGICAL SURVEY OF CHAGAS DISEASE RISK ACROSS LAND-USE GRADIENTS IN CENTRAL PANAMA ...... 49

CHAPTER 5: A MATHEMATICAL MODEL OF HOUSEHOLD-LEVEL RISK OF CHAGAS DISEASE TRANSMISSION BY SYLVATIC TRIATOMINES ...... 79

CHAPTER 1: INTRODUCTION

Neglected Tropical Diseases (NTDs), a group of parasitic, viral, and bacterial diseases considered understudied and underfunded, cause an estimated 530,000 deaths per year and pose a risk to more than a billion people worldwide [1]. Of the seventeen NTDs recognized by the World Health Organization (WHO), seven are vector-borne, and an additional six are zoonotic or involve non-human intermediate hosts. The WHO has outlined goals to reduce the burden of these diseases that range from interrupting transmission to complete eradication [2,3]. Transmission of infectious diseases in humans, especially NTDs, represents a complex interaction between biological and sociological processes. Gaining a complete understanding of both the ecology and the epidemiology of the disease system and their interactions is vital to successful disease management and prevention. Zoonotic and vector-borne diseases (VBDs) are particularly difficult to tackle from a public health perspective, as they are influenced by interactions between humans, insect vectors, the environment, and often multiple reservoir hosts [4,5]. Epidemiological investigations of disease, including household surveys, seroprevalence studies, and risk assessments, reveal the social and behavioral risk factors and transmission potential to humans. On the other hand, understanding the ecology of vectors, such as species distribution and abundance, host preferences, and infection prevalence, can illuminate environmental mechanisms that underlie variation in risks and enhance the implementation of control strategies. Additionally, human- mediated land-use changes, including deforestation, urbanization, and agricultural transformation, can alter disease transmission dynamics [6,7]. However, despite the strong interactions between ecological and epidemiological processes in determining the occurrence of vector-borne and zoonotic diseases, these two facets of infectious disease transmission are typically studied in isolation and with different methodologies. For complex disease systems, failure to integrate these intricately linked processes can prevent effective control, obscure the drivers of outbreak dynamics, and hinder the development of informative mathematical models for prediction of disease spread [4,5,8]. Chagas disease is a vector-borne NTD affecting between 5-10 million people, with an estimated annual economic burden of over $7 billion USD [9–11]. It is spread by dozens of species of blood-feeding triatomines (Hemiptera: Reduviidae), commonly called “kissing bugs”, which are endemic to the Americas [12–14]. Trypanosoma cruzi, a kinetoplastid protozoan parasite and causative agent of Chagas disease, is shed in the feces of triatomines during blood-feeding and must enter the bloodstream through the bite wound or mucous membranes 1

[15]. In humans, Chagas disease has two phases: the acute phase, characterized by constitutional symptoms including fever, malaise, weakness, and, when infection occurs via the eyes, a characteristic inflammation of the eyelid called Romaña’s sign, and a chronic phase wherein the parasite infects the liver, intestines, or heart, potentially causing cardiomyopathy and eventual death [16,17]. Because acute infection is often asymptomatic or symptoms are generalized and mild [16,17], infected individuals often do not seek medical care and may never be diagnosed, making estimates of current human disease incidence and prevalence difficult [10]. The chronic phase of Chagas disease, which can occur 15-20 years after initial infection [10,16], is difficult or impossible to treat with current anti-parasitics [16,18], so vector control remains the most important step in reducing Chagas disease burden. Though Chagas disease greatly reduces human health and survival in many parts of Latin America [12], the ecology of Chagas disease in much of its endemic range remains poorly understood in part due to its complexity. Throughout much of Central and South America, dozens of species of triatomine occur and maintain a sylvatic (i.e., wild) transmission cycle of T. cruzi, which can result in human disease via spillover into the peridomestic and domestic environment [21,22]. In parts of South America, a few vector species, namely Triatoma infestans and Rhondius prolixus, have adapted to carry out the entirety of their life cycle in human dwellings, and thus maintain a ‘domestic’ cycle of disease transmission [23,24]. Intensive studies into the domestic transmission cycle of Chagas disease have elucidated the greatest ecological and epidemiological risk factors for the regions of most intense transmission, particularly the “southern cone” of South America [9,25,26]. Extreme poverty, housing materials that provide habitat for triatomines (e.g., thatch roofs and mud walls), and proximity of domestic animals to human dwellings greatly increase infestation by domiciliary triatomine species [25,27]. Sylvatic transmission, in contrast, most likely occurs when non-domiciliary triatomine species are attracted to human dwellings, perhaps by bloodmeal hosts or light sources [14]. In the case of domestic triatomines, vector control is often achieved by indoor residual spraying campaigns, which have been effective in interrupting disease transmission in many endemic regions [28,29]. While domestic populations of triatomines have been well-controlled using this technique, several challenges still remain [30]. First, insecticide resistance is a threat to the effectiveness of these campaigns [31,32]. Second, sylvatic populations of domestic species of triatomines are often responsible for re-infestation of houses following extermination [33–36]. Third, in many areas, including much of Central America and Panama, no single species of triatomine is entirely domestic [14,23], so insecticide spraying may be a less viable option for control [30]. Finally, research suggests that sylvatic species of triatomine often exhibit 2 considerable behavioral and genetic plasticity and in many cases are able to rapidly adapt to the human environment [37–39]. Human mediated land-use change, which is occurring rapidly in Panama and throughout Central America, can result in human encroachment into sylvatic habitats, potentially increasing contact rates with sylvatic triatomine species [6]. Transmission dynamics are further complicated by changes in host species diversity and abundance, which can affect triatomine infection prevalence (Figure 1.1) [40,41]. Additionally, T. cruzi is a true multi-host pathogen, occurring in more than 150 vertebrate reservoirs, including both domestic and wild mammals [19,20]. In Panama, while no species is entirely domestic, the palm-dwelling Rhodnius pallescens is considered the most important vector of T. cruzi to humans [42,43]. Several other triatomine species, including Triatoma dimidiata and Panstrongylus geniculatus, are collected around human dwellings in high numbers and may be important vectors of T. cruzi to humans, although understanding of their potential role in disease transmission has been neglected [44–46]. Owing to the nature of triatomine blood-feeding, which generally takes place at night on sleeping hosts, the transmission of T. cruzi to humans by sylvatic vectors is still most likely to occur in the domestic environment. An estimated 18,000 people are currently infected with Chagas disease across Panama, [10] although difficulties in diagnosis combined with a lack of a national reporting system make accurate estimates of total prevalence difficult. Studies in some areas have found seroprevalence (i.e., the prevalence of a pathogen in a population measured from blood samples) rates ranging from 2% to 30% in humans living in homes where kissing bugs have been collected [45,47]. This great variation in infection prevalence indicates that transmission is not uniform across the landscape but may vary greatly both between and within communities. Panama, like much of the rest of Central America, is undergoing rapid anthropogenic land- use change [40], including deforestation and urbanization, that can increase human exposure to disease vectors [6,48]. Which factors attract sylvatic vectors to the domestic setting, and even which species of triatomine are important vectors of T. cruzi in Panama, remain unknown. Also unknown is the variation in parasite prevalence across human land-use gradients, the contributions of domestic and wildlife host species to disease risk, and how these factors interact to determine heterogeneity in the risk and incidence of Chagas disease. The WHO has stated the goal of reduction or elimination of vectorial transmission of Chagas disease throughout its endemic region by 2020 [2,3]. Meeting these ambitious goals will require understanding the complexities of the sylvatic cycle of Chagas disease, which, in recent years, has become an increasing challenge for Chagas control [21,34,49]. Understanding the 3 distribution, abundance, and extent to which these sylvatic triatomine species occur in homes, the variation in their infection prevalence, and which epidemiological factors are predictive of their occurrence, is critical to controlling the sylvatic transmission cycle both in Panama and throughout the Americas where multiple triatomine species co-occur. This dissertation aims to answer four questions integral to the understanding and eventual control of Chagas disease in Panama: 1. Can methods for the capture of triatomines be improved to allow researchers to monitor sylvatic populations and collect sufficient quantities of specimens for pathogen analysis? 2. What are the contributions of under-studied sylvatic species to exposure risk, both in terms of T. cruzi infection prevalence and overall vector potential? 3. What are the ecological and epidemiological risk factors that govern Chagas disease exposure risk, and how do these vary across gradients in human land-use? 4. Can this information be used to predict mathematically the probability of human exposure to Chagas disease and the population-level incidence of infection in Panama? While substantial research has been performed on monitoring domestic populations of triatomines [33,35,50], few studies have addressed population dynamics of sylvatic species. Standard collection methods for sylvatic triatomines are labor- and time-intensive, often expensive, and there is a need for a more efficient method [51,52]. In Chapter 2 I developed a lightweight, inexpensive, easily portable trap based on one often used in the collection of wood- boring insects [53,54] and evaluated its effectiveness in attracting and collecting sylvatic triatomines. I found that, when fitted with an ultraviolet light, these cross-vane panel traps were as effective as a traditional light-and-sheet combination in attracting and capturing triatomines, and thus may provide an important tool by which both researchers and public health professionals can monitor sylvatic triatomine populations in the future [55]. Monitoring sylvatic triatomine populations is an important first step in determining their relative contributions to human Chagas disease risk. There is also a need for a better understanding of the differences in T. cruzi infection prevalence across species, and how this may contribute to disease risk. In Panama, while there are at least eight species of triatomine known to be naturally infected with T. cruzi [13,14], only two, Triatoma dimidiata and Rhodnius pallescens, are often considered important disease vectors [56,57]. Few studies have examined the other sylvatic species in this region [57,58]. In Chapter 3, I tested 314 triatomines collected across two years in Panama for infection with T. cruzi to examine differences in infection prevalence across species and calculated a metric of vector potential based on infection prevalence and collection rate. I found differences in infection prevalence across the seven 4 species of triatomine tested, which indicate the need for a better understanding of the natural history, host preferences, and transmission capabilities of these sylvatic triatomine species to determine their role in the human disease transmission cycle. With a better understanding of the differences in infection prevalence of various sylvatic triatomine species, I next addressed the ecological and epidemiological risk factors that may contribute to human exposure risk. While the factors that govern house infestation in areas where triatomines are domestic have been explored extensively [25,27,52], the ecological and epidemiological factors that govern attraction of sylvatic triatomines to human domiciles remain poorly understood [22]. In Chapter 4, I used an epidemiological survey, combined with sixteen- months of in-home triatomine collection, to determine important factors which may lead to triatomine attraction, and possible Chagas disease exposure, across three urban-to-rural land use gradients in central Panama. I found that both ecological and epidemiological factors, including land use and the number of domestic and wild animals around the home, significantly contribute to the number of triatomines collected around the home. Finally, in Chapter 5, I expanded upon a previously developed mathematical model [59] to estimate the individual risk of Chagas disease transmission at a household level, which can then be extrapolated to predict annual Chagas disease incidence at a land-use level in Panama. The predictions from this model are consistent with WHO estimates of the number of new infections annually in Panama and allow for a more sophisticated understanding of risk based on land-use. This type of modeling is an important first step in developing targeted control strategies across heterogeneous landscapes, which will be essential to achieving the World Health Organization’s 2020 goal of interrupting intra-domiciliary transmission throughout the range of Chagas disease [2].

Figure

Figure 1.1 Infographic showing potential ecological and epidemiological risk factors for triatomine invasion across an urban-to-rural land use gradient. Blue represents domestic animals, Red represents wild animals.

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CHAPTER 2: AN EXPERIMENTAL EVALUATION OF CROSS-VANE PANEL TRAPS FOR THE COLLECTION OF SYLVATIC TRIATOMINES (HEMIPTERA: REDUVIIDAE)

Abstract

Due to the limited understanding of the sylvatic cycle of Chagas disease transmission, an efficient method to attract and capture sylvatic triatomines (Hemiptera: Reduviidae) is essential to monitor human exposure risk. Current collection methods for sylvatic species, though effective, are labor-and time-intensive. This study evaluated whether modified cross- vane panel traps (commonly used in forest entomology) can be used to attract and capture flying life-stages of sylvatic triatomines and whether a commercially available lure is effective in attracting sylvatic triatomines in the field. We evaluated four trap treatments in both the wet and dry seasons in central Panama: a cross-vane panel trap fitted with an ultraviolet (UV) light, a cross-vane panel trap fitted with a commercially available human-volatile lure, a cross-vane panel trap fitted with both a UV light and a human-volatile lure, and a white sheet fitted with a UV light (a standard collection method) as a control. A total of 45 adult Rhodnius pallescens were captured across 10 nights of trapping representing 112 trap-nights. There was a significant overall effect of trap type on collection success; sheet traps collected more triatomines than lure traps, and there were no differences between the sheet trap and the UV trap, nor between the sheet trap and the UV+lure trap. The lure-only trap did not capture any triatomines in this study. These results indicate that cross-vane panel traps with a UV light are as effective as a sheet trap but offer the advantage of requiring less time and effort to maintain and monitor.

Introduction

Surveillance of blood-feeding insect vectors using consistently replicable collection methods is an important component of vector-borne disease management and vector control. Triatomine (Hemiptera: Reduviidae) “kissing bugs” are blood-feeding insects capable of transmitting the parasite Trypanosoma cruzi, the causative agent of Chagas disease [1]. Some triatomine species have adapted to complete their life cycles within homes, and substantial research has been performed on collection methods allowing researchers to monitor these domestic populations [2–4]. However, few studies have addressed population dynamics of sylvatic (i.e., wild-living) triatomine species, particularly with respect to how fluctuations in these

1This chapter appeared in its entirety in the Journal of Medical Entomology and is referred to later in this thesis as Updyke, EA., Allan BF. 2017. An experimental evaluation of cross-vane panel traps for the collection of sylvatic triatomines (Hemiptera: Reduviidae) Journal of Medical Entomology 55(2):485-489. doi.org/10.1093/jme/tjx224 13 populations may affect disease transmission risk to humans. Current methods for monitoring sylvatic triatomine populations can be labor-intensive, time-consuming, and expensive [5] and there is need for more efficient collection methods [6,7]. Methods for intradomiciliary triatomine collections include the pitfall trap [8–10], baited or unbaited sticky traps [4,11], and timed manual searches [2]. To capture sylvatic triatomines, typically either a standard insect light trap (i.e., a light reflected on a white sheet) [12,13], or Noireau trap (i.e., a sticky trap baited with a live animal, such as a mouse) is used [14,15]. Both methods can be labor- and time-intensive and, in the case of Noireau traps, require maintaining and deploying live animals in the field. Additionally, while Noireau traps have been effective in trapping triatomines in palms and hollow trees [14,15], in some cases they have not been as effective in other habitats [16]. In Panama, the vast majority of vector species, including Rhodnius pallescens, the most important vector of Chagas disease in Panama, are sylvatic [17,18]. Noireau traps are commonly used for the capture of sylvatic R. pallescens from their principal habitat of palm crowns [19–21]. Various alternatives to live baits have been tested including baker’s yeast

[8,9,22] and synthetic host-odor mimics with CO2 [23]. Recently, a multimodal bait has been shown to attract Triatoma infestans in the laboratory and the field and Rhodnius prolixus in the laboratory [24]. Additionally, a commercially available lure designed to attract Aedes spp. mosquitoes attracted Rhodnius prolixus under laboratory conditions [25,26]. However, these alternative baits have not yet been tested for other sylvatic species of triatomine, nor extensively tested under field conditions. This study had two main objectives: first, to determine if a cross-vane panel trap (Figure 2.1), commonly used in forest entomology [27,28], could be modified to attract and capture flying life-stages of sylvatic triatomines, and, second, whether a commercially available lure would be effective in attracting sylvatic triatomines in the field. In total, four trap treatments were experimentally evaluated across wet and dry seasons in central Panama where R. pallescens and several other species of sylvatic triatomines occur.

Methods

Study Site This study was conducted in Llanito Verde, Panama Oeste, Panama. This community was chosen due to previous collection of several sylvatic triatomine species within and around the community (unpublished data). Three transects were established approximately 230-330m 14 apart at the edges of residential properties in small pockets of natural habitat consisting of trees and other vegetation, including Attalea butyracea palms, which are known to be an important habitat for Rhodnius pallescens [1,21]. Based on the availability of these habitats, the three transects were 94m, 85m, and 62m long, and one of each of the four different trap types were equally spaced across each transect with a minimum distance of 20m between traps. Trap Description & Deployment Cross vane panel traps consisted of two 30X30cm squares of corrugated white plastic coated in Fluon®, notched and fitted together to form an X pattern, attached to a large funnel made of the same plastic. This funnel was attached to another 30cm-diameter round funnel fitted with a 950ml-wide mouth jar. Cross-vane traps with three different potential attractants were tested: a cross-vane panel trap fitted with a UV light (Flourescent, U-shaped FUL 4-pin GX10Q 18W or 15W black light, hereafter, “UV trap”), a cross-vane panel trap fitted with a chemical lure designed to mimic human skin volatiles (“BG-Lure-Cartridge”, Biogents, Regesberg, Germany, hereafter, “lure trap”), and a cross-vane panel trap fitted with both a UV light and a chemical lure (hereafter, “UV + lure trap”) (Figure 2.1). A white sheet (approximately 159X260cm) suspended vertically between two trees and illuminated by a UV light (hereafter, “sheet trap”), served as a control as this is commonly used for capturing sylvatic triatomines [12,13,29,30]. Under MiAmbiente permit SC/A-43-16, traps were deployed for six nights in July and August 2016 during the wet season and four nights in December 2016 at the beginning of the dry season. The location of each trap on each transect was rotated every night, such that each trap was at each location at least once per season, using a modified Latin Square design. One of each of the four trap types were deployed on each transect for 3.5 hours per night, starting approximately 15 minutes before sunset, as these hours immediately following dusk are when triatomines tend to be most active [13]. Due to occasional failure of lights, total trap-nights were: sheet trap (27 trap-nights), UV trap (27 trap-nights), lure trap (30 trap-nights), and UV + lure trap (28 trap-nights). Two wattages of UV light were used (15W and 18W) due to equipment failure and lack of access to replacement bulbs. All UV lights were powered with 12V rechargeable DC batteries and fitted with timers that activated and deactivated simultaneously. Sheet traps were either monitored continuously or checked at 15-20 minute intervals; all triatomines on sheets were collected immediately. Cross-vane panel traps were not monitored throughout the night, and all captured triatomines were retrieved at the end of the 3.5-hour interval. Captured triatomines were stored in 95% ethanol and identified according to Lent and Wygodzinsky (1979). 15

Statistical analysis We conducted statistical analysis in R using the lme4 package [31]. A linear mixed model was performed on log +1 transformed capture data. Trap type (four types) and season (wet vs dry) were included as fixed variables while location within transect was included as a random variable. Similar analyses (linear mixed models on log+1 transformed data) were conducted to determine if wattage of light and whether sheet traps were continuously monitored contributed significantly to differences in trap success.

Results

We captured a total of 45 adult triatomines, 20 female and 25 male Rhodnius pallescens, across 112 trap-nights. There was an overall effect of trap type on collection success (F= 3.68, p=0.015; Figure 2.2), and no difference in collection across season (F= 3.28, p=0.07; Figure 2.3). Post hoc analyses revealed sheet traps collected more triatomines compared to the lure traps (Tukey’s HSD: p=0.008), which did not capture any triatomines throughout the study. There were no differences between the sheet trap and the UV trap, or between the sheet trap and the UV+lure trap (Tukey’s HSD: p=0.85 and 0.62, respectively). Sheet traps were either monitored continuously (12 trap-nights) or checked every 15-20 minutes (15 trap-nights); there was no difference in capture rates based on continuous versus periodic monitoring (p=0.83). Finally, there was no difference in capture rates based on wattage after controlling for the effect of transect (p=0.21).

Discussion

An efficient method for attraction and capture of sylvatic triatomine species is essential to monitor populations in areas where human risk may exist. This study captured a total of 45 Rhodnius pallescens using primarily unmonitored traps fitted with a UV light. The majority of these were collected during the dry season, although the effect of season on capture success was not significant. Traps captured two other species of triatomine (Triatoma dimidiata and Panstrongylus geniculatus) during preliminary testing (unpublished data); however, only R. pallescens was captured during the experiment described here. Throughout the study, neither triatomines nor bycatch were collected in traps fitted with only a chemical lure. However, traps fitted with either a UV light or with both a UV light and chemical lure were equally effective in

16 attracting and trapping triatomines, and the capture rate of these traps was similar to those of a standard sheet trap. Capturing sufficient numbers of triatomines to estimate infection prevalence and population dynamics can be a time- and labor-intensive process. Noireau traps, while effective with capture rates of 2.25 [20] to 2.5 [14] triatomines per trap, require extensive equipment, maintenance of live animal colonies, and knowledge of nesting sites of triatomines [14,15]. The standard method of collection using a UV light yields capture rates of 0.3-0.4 [12,29], or more [13,30] triatomines per trap, but requires constant or frequent monitoring by observers to capture and remove any triatomines that land on the sheet, limiting the overall number of traps that can be deployed. This study used a trap with the same attractant properties, yielding a capture rate of 0.54 triatomines per trap (considering only traps with UV lights), while allowing for triatomines to be captured in a collection container without the need for monitoring or intervention. Triatomines flying toward the light collide with the sides of the cross-vane panels and fall down the Fluon-coated surface into the collection container [28]. Limitations to this study include the use of UV lights as the source of light. Although UV light is often used as an attractant for a variety of insects, including triatomines [29,32–34], it may not be the most relevant light source for peridomestic environments where other lights sources dominate. Additionally, Minoli and Lazzari (2006) found that triatomines may be more attracted to white light wavelengths compared to other wavelengths of light (but see Pacheco- Tucuch et al. 2012). Future studies should test a variety of light types, but in the interim these traps using UV light can be effective, and, by using a potentially less attractive light source we may have underestimated their overall effectiveness. The chemical lure tested in this study is effective in a laboratory environment [25,26]; however, it was not effective at capturing triatomines in this study. This lure may be effective at close range but may not be effective at the long ranges necessary to attract flying triatomines in the field. While the lure did not increase capture rates of triatomines when used in combination with a longer-range UV light lure, it is possible that adult triatomines attracted to lights are in search of mates, as male R. prolixus have been shown to initiate flight in response to female pheromones, [37] and therefore not actively host-seeking. This could make the addition of host odors to a light trap ineffective. Additionally, the lure used in this study has been tested in mosquito traps using a fan to aid in odor dispersal [38,39]. It remains to be tested whether this lure could function as an attractant for sylvatic triatomines in combination with a fan. Future studies should compare this lure with a more similar attractant, such as a Noireau trap, to establish whether it could be effective in attracting sylvatic triatomines under field conditions, 17 test pheromone lures for attracting flying adults, and potentially include a fan for odor dispersal on the traps. Understanding and monitoring sylvatic triatomine population dynamics is an important aspect of disease management for several reasons. First, in many areas, re-infestation of houses following insecticide treatment is thought to occur from sylvatic populations [2,40–42]. Second, some sylvatic species show signs of ongoing domestication [43–45]. Finally, in some Chagas endemic areas, including Panama, human transmission may be driven by sylvatic species [46–50]. This study represents a new implementation of a trap traditionally used in forest entomology in a medical entomology framework. The traps themselves are lightweight (<1.15 kg without battery), easily transportable, and require the same light and battery as a sheet trap. Furthermore, they can be deployed and captured bugs can be collected without the need for continuous monitoring. These findings present exciting possibilities for research in the field of vector biology, but they also offer potential for an important surveillance tool for public health applications.

Figures

Figure 2.1 Representation of Trap types. Drawings not to scale

Figure 2.2 Mean number of triatomines captured per night by trap type. Error bars represent one standard error

Figure 2.3 Mean number of triatomines captured per night by season. Error bars represent one standard error

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CHAPTER 3: INVESTIGATION OF TRYPANOSOMA CRUZI INFECTION RATES ACROSS SYLVATIC TRIATOMINE SPECIES IN PANAMA

Abstract

Chagas disease is transmitted by dozens of species of blood-feeding triatomine “kissing bugs” (Hemiptera: Reduviidae). While several triatomines complete their life cycle in human- built environments, most species live primarily in natural environments and come into contact with humans only transiently. The contribution of sylvatic species of triatomine to human disease risk is not well understood. In Panama, several species of triatomine are naturally infected with Trypanosoma cruzi, the causative agent of Chagas disease, but only two (Rhodnius pallescens and Triatoma dimidiata) are considered important disease vectors. I used a combination of field and molecular approaches to explore the relative contributions of sylvatic triatomine species to human Chagas disease risk in central Panama. I tested seven species of triatomine for infection with T. cruzi using established PCR and qPCR methods. Additionally, I determined an estimate of vector potential for five species of triatomine, based on collection rates from households and T. cruzi infection prevalence. In total, 62.9% of the 305 specimens tested were positive for T. cruzi. Panstrongylus geniculatus, the second most commonly collected species after R. pallescens, had a significantly higher infection prevalence compared to R. pallescens. While R. pallescens was captured in households at higher rates compared to other species, they accounted for only 34% of the total vector potential of all species based on overall abundances and T. cruzi infection rates. These results indicate that sylvatic species may contribute to human disease risk more than previously recognized in Panama.

Introduction

Chagas disease is a Neglected Tropical Disease (NTD) endemic to the Americas that affects 5-10 million people worldwide [1,2]. Caused by the kinetoplastid parasite Trypanosoma cruzi, Chagas disease can be transmitted by the feces of blood-feeding triatomines (Hemiptera: Reduviidae) to more than 150 mammalian hosts [3–5]. In much of the Southern Cone countries of South America, Chagas disease is spread by Triatoma infestans, a species of triatomine that often lives in the cracks of walls and floors within human dwellings and can complete its entire life cycle in human-modified environments [3,6]. Elsewhere in Central and South America, two other species of triatomine, T. dimidiata and Rhodnius prolixus, are the major vectors of Chagas 26 disease; these species also have peridomestic populations throughout much of their range [7– 9]. However, there are dozens of other species of triatomine that live primarily in natural environments that are also infected with T. cruzi [3,10–13]. Additionally, even primarily domiciliary species of triatomine maintain sylvatic populations that are often responsible for reinfestation of houses following local eradication [14–17]. Sylvatic species of triatomines often are overlooked as major contributors to Chagas disease burden, despite that they are attracted to human dwellings at night by lights or the availability of bloodmeal sources [13]. Many of these species are poorly studied and we know little about their natural history. Recent studies suggest substantial plasticity in terms of host preference and life-history adaptations, making sylvatic species more likely to colonize human environments than previously realized [12,13,18–20]. There is also increasing evidence that several sylvatic species are invading and establishing in peridomestic environments [8,21–25], which could lead to shifts in Chagas disease risk. Human-mediated land use changes, including rapid deforestation and agricultural transformation happening across many Chagas-endemic regions, likely exacerbate this problem, putting more people in closer contact with sylvatic habitats [26–29]. These changes highlight the potential epidemiological importance of sylvatic species and the need to investigate their contribution to disease transmission cycles [8,30,31]. In Panama, no species of triatomine is considered entirely domestic, but there are at least eight species naturally infected with T. cruzi, and two (R. pallescens and T. dimidiata) are considered significant vectors of disease [32–34]. Rhodnius pallescens is considered the most important vector of Chagas disease throughout Panama, while T. dimidiata is important in areas of higher elevation [32,34]. Although none of these species live entirely in domestic environments, about 1 in every 200 people in Panama (~ 18,000 individuals) are currently infected with T. cruzi, with an estimated 175 new infections each year [1]. These estimates are likely low due to high rates of underreporting and difficulties in diagnosis, coupled with the lack of a national active surveillance program [32,35]. In Panama, multiple studies using household volunteers as participants have found up to 20% of bugs entering homes to be species other than R. pallescens [33,34,36]. This has led to the conclusion that R. pallescens is the most important regional vector of T. cruzi, although a quantitative analysis that addresses both occurrence and T. cruzi infection rates of sylvatic species is needed to understand the contributions of other triatomine species to human disease risk. Through a combination of field investigation and molecular approaches, this study assessed the relative potential contributions of sylvatic species of triatomine to human risk of Chagas disease exposure in Panama. I tested seven species of triatomine, field-collected 27 throughout Panama, for T. cruzi infection, examined whether there are differences in infection prevalence across species and sex, and determined, based on collection rates and infection prevalence, a metric of vector potential for each of five triatomine species.

Methods

Triatomine collections Triatomines were collected from January 2014 to December 2016 throughout Panama across four habitat types: natural areas, rural areas, semi-urban areas, and urban areas (Table 3.1). Habitat types were defined based on proportion of forest cover determined via Google Earth satellite imagery (accessed in May 2015), land use and forest cover maps developed by MiAmbiente in 2012 and human population densities determined from Panama census records. Natural areas had 60 to 100% forest cover, urban areas were 30 to 70% urban cover, and semi- rural and rural areas had differences in population size. Four different collection methods were used in this study: mercury vapor lights, ultraviolet (UV) lights, hand-collection, and household collection (Table 3.1). Bugs collected via mercury vapor and UV lights were either captured as by-catch during other entomology studies, or from a study examining the effectiveness of cross- vane panel traps for attracting and capturing triatomines [37]. Hand-collected bugs were found on walls outside and inside of human dwellings at various field locations of the Smithsonian Tropical Research Institute across Panama. Household collections were performed as part of a larger eco-epidemiological study of Chagas disease risk across an urban-to-rural human land- use gradient in central Panama (for full study details, see Chapter 4). Briefly, 182 households in nine different communities in central Panama spanning three urban-to-rural land-use gradients participated in household collections from June-July, 2015 until December, 2016. Household volunteers were given a urine collection container affixed with photographs of five species of triatomine, a plastic zip-top bag, 5 ml vials filled with 95% ethanol, and instructions on how to safely collect triatomines found in the home. Participants were asked to collect any triatomines found inside or outside the home during the study period, preserve them in ethanol, and note the date and location of collection (inside or outside the home). Participants had collection containers for an average of 512 days (+/- 11 days) and researchers returned to collect filled vials at four different time points throughout the study period: August 2015, January 2016, July 2016, December 2016. All triatomines were measured (total body length and wing length), sexed, identified to species per Lent and Wygodzinsky (1979), and stored at room temperature in 95% ethanol. 28

Nine triatomines were deposited as voucher specimens in the insect collection at the University of Panama as per export permit requirements and were therefore not tested for T. cruzi infection. Triatomines collections for this study were performed under MiAmbiente collection permit numbers SC/A-16-15, 6-16 and 43-16 and exported under MiAmbiente export permits SEX/A-122-15, 85-16, and 3-17. Imports were approved by the USDA under Veterinary Permit 130187 and CDC PHS Permit 2016-08-052 and 2016-12-092. Trypanosoma cruzi infection testing Triatomine specimens were tested for presence of T. cruzi using established PCR and qPCR detection methods [38–42]. Whole triatomines were washed in 50% bleach for 15 seconds, followed by a 15 second rinse in distilled water, and were placed on a sterile Petri dish. The head and thorax were removed from the abdomen using flame-sterilized forceps, and the last two segments of the abdomen were cut off using flame-sterilized scissors. The abdomen was then cut open longitudinally and the guts were scored for bloodmeal status. Bloodmeal scores were as follows: 1- no blood present, guts extremely desiccated or not present; 2- guts present but no trace of bloodmeal; 3- some traces of bloodmeal present and identified as dark specks visible in guts; 4- blood evident, but small amounts or very dried; 5- blood evident and fresh or copious. DNA extractions were performed on the same day as dissections on the entire gut and its contents as well as the last two segments of the abdomen, using the Qiagen DNeasy Blood and Tissue kit (Qiagen, Hilden, Germany) according to manufacturer’s instructions, with the addition of an overnight lysis step. DNA extraction success was confirmed using the Qubit dsDNA HS Assay Kit (Life Technologies, CA, USA) to determine overall DNA concentration, and samples were stored at -80°C until further testing. Positive controls of T. cruzi lineages TcI and TcIV were obtained from Texas A&M University under Materials Transfer Agreement 4983/17. Real-Time qPCR was performed using Cruzi1/Cruzi2, along with Cruzi3 probe (5’-FAM; 3’-NFQ), which amplifies a conserved region of T. cruzi satellite DNA and has been shown to be effective in amplifying DNA from degraded samples [43,44]. Specifically, 5 µL of extracted DNA were used with Bio-Rad iTaq Universal Probes Supermix (Bio-Rad, CA, USA), in a total reaction volume of 20 µL, under the following reaction conditions: 95°C for 3 min followed by 40 cycles of 94°C for 20 seconds and 63°C for 20 seconds. Samples were considered potentially positive at cycle threshold (Ct) values of less than 35 [38,43]. All samples were further subjected to PCR using primers TC121/TC122, which target kinetoplastid minicircle DNA [42]. Briefly, 2 µL of DNA were combined with Phusion High Fidelity DNA Polymerase and master mix (New England Biolabs, MA, USA) in a total reaction volume of 30 µL, with the following reaction conditions: an initial denaturation at 98°C for 30 29 seconds followed by 98°C for 10 seconds, 67°C for 30 seconds, and 72°C for 30 seconds for 35 cycles, finished at 72°C for 7 minutes. PCR results were visualized on a 1.5% agarose gel stained with GelRed (Biotium, CA, USA). Samples were considered positive only if they had a Ct value of less than 35 on qPCR and had a visible band with PCR. Statistical analysis To determine the variables to include in final analyses, I first examined whether associations were present between collection methods, habitat type, or bloodmeal score and either triatomine species or sex using Fisher’s exact tests with a Monte Carlo simulation of 2,000 replicates. For statistical analysis, I collapsed collection methods into three categories: hand collected, household collected, and light collected (i.e. combining UV light and mercury vapor light). Due to limitations in sample size, and non-standardized collection efforts across habitat types and collection methods, I did not attempt further analyses to evaluate differences in species composition across habitat type or collection method. To examine the main outcome of T. cruzi infection status (i.e., infected or uninfected), I used a logistic regression model, first comparing an unmixed logistic regression against a mixed model that included collection location within each habitat type as a random effect. The full model included sex, species, bloodmeal status, collection method, and habitat type as fixed effects as well as interactions between species and sex and species and habitat type. Model comparisons were assessed via likelihood ratios to determine a best-fitting model. All statistical analyses were conducted in R version 3.4.3. Calculating Vector Potential I calculated a measure of vector potential for each of the five species collected during the eco-epidemiological study across urban-to-rural land use gradients. These were specimens for which I could determine collection rates and which likely are the most relevant to human exposure risk as they were all captured within and around homes. I calculated vector potential (V) for each species (i) according to equation 1, where R = collection rate (i.e., the number of bugs collected per house per unit time), and P = T. cruzi infection prevalence.

푅푖푃푖 푉푖 = 푖 (Equation 1) ∑푖=1 푅푃 This value represents a ratio of the potential contribution of each species to overall risk of infection for humans, considering collection rate and infection prevalence for each species.

Results

I collected 314 triatomines, representing seven species from four genera (Rhodnius, Panstrongylus, Triatoma, Eratyrus), across two years and seventeen locations, 305 of which (97%) were tested for presence of Trypanosoma cruzi (Table 3.2). Forty-five triatomines were collected as part of a UV trap study [37] and 197 were obtained through household collections as part of an eco-epidemiological investigation of Chagas disease risk in central Panama (see Chapter 4). The remaining 72 triatomines represent a convenience sample of triatomines collected throughout the two-year period in Panama. There were significant associations between collection method (Fisher’s exact test: p<0.001) and species as well as habitat type (Fisher’s exact test: p<0.001) and species (Table 3.1). Because collection effort was not standardized across habitat type nor collection method, I did not further evaluate the associations between species and habitat or collection method. There was a marginally significant difference in sex across species (Fisher’s exact test: p = 0.051; Table 3.2, Figure 3.1), with more males infected than females. There were no significant differences by sex in collection method (Fisher’s exact test: p=0.17) or habitat type (Fisher’s exact test: p=0.52). There was a significant difference in bloodmeal scores by sex, but not by species (Fisher’s exact test; p=0.003 and p =0.15, respectively; Table 3.3). Males were more likely to have some evidence of a previous bloodmeal, but no more likely than females to have evidence of a fresh or recent bloodmeal. Overall infection prevalence was 62.9% for all species combined (Table 3.2, Figure 3.1). An unmixed logistic regression model was a better-fitting model than a mixed logistic regression, and the final model included sex, species, habitat type, and their interaction (Table 3.4). There was no significant interaction between sex and species, nor any effect of bloodmeal score or collection method; these covariates were not included in the final model. Of the two species with the largest sample sizes, P. geniculatus had a significantly greater overall infection prevalence (85%) compared to R. pallescens (58%) (Estimate: 1.94, Std Error: 0.81, p=0.017). While the highest overall infection prevalence was seen in T. dimidiata (100% of the bugs tested were infected), and 81.8% of P. rufotuberculatus tested were infected, these and the other three species tested had small sample sizes and infection prevalence in each of these species may not be representative. No other species was found to be significantly more or less infected compared to R. pallescens, likely due to small sample sizes. Although males had significantly higher infection prevalence compared to females (Estimate = 2.34, Std Error: 0.31, p<0.001), the difference in infection prevalence between 31 males and females was greatest in R. pallescens (87% vs 33%, respectively) and far less pronounced in other species (Figure 3.1). Because all other species had substantially lower sample sizes compared to R. pallescens, additional logistic regression analyses were performed to examine the effect of species and sex on infection prevalence for all species other than R. pallescens. In these analyses, no significant effect of sex was found (Single Logistic Regression Analysis of Deviance: Chisq: 0.63, df: 1, p=0.42), and there remained a significant effect of species and an interaction between species and habitat type (Multiple Logistic Regression Analysis of Deviance: Chisq: 18.2, Df: 5, p=0.003 and Chisq: 6.1, Df: 2, p =0.05, respectively). In R. pallescens alone, males were significantly more infected than females (Single Logistic Regression: Estimate: 2.6, Std Error: 0.34, p<0.001), suggesting that the significant effect of sex seen when analyzing all species combined is driven by R. pallescens. Vector potential was calculated for the five species of triatomine captured through the household collections, for which collection rates could be estimated. Vector potential represents the relative potential contribution of each species to disease risk based on vector collection and infection rates (equation 1). While R. pallescens was found to have the highest overall vector potential, all sylvatic triatomine species appear to contribute to human exposure risk (Figure 3.2).

Discussion

I examined variation in T. cruzi infection prevalence in seven species of triatomine collected over two years in central Panama. Overall, I found significant differences in infection prevalence among species; in many cases other sylvatic species of triatomines had higher prevalences than R. pallescens¸ commonly considered the most important vector species in Panama [27,28,36]. Although I detected a significant difference in infection prevalence only between the two most commonly collected species, R. pallescens and P. geniculatus, the failure to find differences between other species is likely due to low sample sizes. These results complement previous studies examining the infection prevalence of R. pallescens [36,47,48] and T. dimidiata [49], and provide unique insight into patterns of infection in other triatomine species in Panama [50]. Most studies on the transmission cycle of Chagas disease focus on domestic populations, but understanding the contribution of the sylvatic cycle to human disease risk is critical [25,30,31]. Though the regions that suffer the greatest burden of Chagas disease tend to have high rates of transmission via domestic vectors [1], there is persistence of transmission in 32 areas where indoor residual spraying campaigns have previously eliminated intradomiciliary populations of triatomines [45,46], and transmission in areas such as Panama where domestic populations of triatomines have not been reported. Thus, improved understanding of the role of sylvatic species in transmission of T. cruzi is needed to fully address the burden of Chagas disease on human health. While habitat type alone did not have a significant effect on infection prevalence, there was a significant interaction between species and habitat type, suggesting that infection prevalence by species may vary across habitats. Due to unequal collection efforts across habitat types I cannot discern whether this difference reflects differences in biology or is due to sampling bias. Many triatomines are thought to exhibit specific host [3,13,51] and habitat preferences (e.g., Panstrongylus spp. preferentially inhabit tree holes or animal burrows, Rhodnius spp. are most often found in the crowns of palm trees, and Triatoma spp. associate with rock piles or tree holes [3,52]). Previous studies in Panama have shown abundance and infection prevalence in R. pallescens vary across habitats [28,47], and are strongly associated with the presence of Attalea palms [27]. However, triatomines are also highly adaptable. Despite apparent differences in this study in species abundances across habitat types, the majority of bugs collected, including all those collected by ‘hand’ even in predominantly natural areas, were collected inside or on the outside walls of human dwellings, suggesting that many of these sylvatic species may come into more contact with humans than previously thought. This frequent contact with the peridomestic environment may be a driver of domiciliation of sylvatic species [8], which could have important implications for future Chagas disease risk. In some cases, sylvatic species have very large geographic ranges and aspects of their natural histories may vary across their range, from the habitats and bloodmeal sources they prefer to their infection prevalence [3,9]. In the case of T. dimidiata, which is often found invading peridomestic and domestic habitats, infection rates across its geographic range vary from 0% to 47% [9]. In Panama, infection prevalence has been previously estimated at 43% in sylvatic T. dimidiata collections, while peridomestic collections yielded an infection prevalence of 21% [53]. All of these estimates are lower than in this study, which found 100% infection in nine specimens of T. dimidiata. While this is a low sample size, the potential for high infection prevalence in T. dimidiata coupled with previous findings of up to 24% of T. dimidiata bloodmeals coming from human sources emphasize the need for further study of this species [49]. The genus Panstrongylus is of particular interest for several reasons. First, P. geniculatus was the second most commonly collected species in this study; the majority of the 33

P. geniculatus specimens were collected in Gamboa, Panama, a former Canal Zone town located in the center of Soberanía National Park in Panama. While in many cases this species is considered sylvatic, it may have a strong attraction to human dwellings, and, indeed, there is evidence of this species frequently invading the domestic and peridomestic environment in parts of South America [22,23,54–58]. These trends, when seen in light of the significantly higher infection rate in P. geniculatus in this study, indicate that this species may contribute to the human transmission cycle in Panama. Additionally, I collected P. rufotuberculatus, often considered a purely sylvatic species [34,53,59], 10 times around houses where they exhibited high infection rates. Other species, including P. humeralis and P. rufotuberculatus, were collected in forested natural areas where humans also live, often by opportunistic collection around lights outside and inside of human dwellings. In fact, the only species in this study that was collected entirely by light trapping and in forested areas away from human dwellings was T. dispar. The largest difference in infection prevalence across sex was seen in R. pallescens, with over 52% greater infection prevalence in males as compared to females. This pattern was not seen in P. geniculatus¸ and there was no significant effect of sex on infection prevalence in models that excluded R. pallescens. Despite low sample sizes in P. rufotuberculatus, a similar pattern may exist (Figure 3.1), so additional testing of these other sylvatic species is warranted. While previous studies have found differences in both infection prevalence and bloodmeal score by sex, researchers have found evidence for both male- and female- skewed infection rates for different species [60,61]. In sylvatic populations of some triatomine species, males are more mobile and often more abundant compared to females, which could lead to encounters with a wider range of potential reservoir hosts across a variety of habitat types, leading to higher infection rates [60,62]. There is also evidence of differential household invasion by males and females across different species [25,60], so further investigation is needed to understand the epidemiological importance of male versus female triatomines. In this study, approximately 9.2% of females and 9.9% of males had very full or very fresh bloodmeals (scores of 4/5) in their guts at the time of capture. Males were slightly more likely to have some evidence of a previous bloodmeal (score of 3), while females were slightly more likely to be starved (scores of 1/2). While R. pallescens was captured at higher rates compared to other species, they accounted for only 34% of the total vector potential of all species in the household collections based on overall abundances and T. cruzi infection rates. This low percentage indicates that other sylvatic species may be contributing more than previously anticipated to disease risk. 34

Some earlier studies in Panama have found very small proportions (i.e. less than 1%) of all bugs collected in household searches to be species other than R. pallescens [34], while other studies have found up to 20% of the bugs collected in homes to be other species [33]. Despite the fact that studies on the ecology and epidemiology of Chagas disease in Panama date back to the 1930s (e.g. 37,61,63), much is still unknown about the contributions of sylvatic vector species to the human transmission cycle [32]. In several cases, species found in this study within homes in rural and semi-rural areas are noted in the literature as existing only in pristine natural environments and thus as posing little to no risk of human transmission [32]. Other species, especially T. dimidiata, are often cited as being common in high elevation or western regions of Panama that are less disturbed than the areas typical of R. pallescens habitat [27]. One important limitation of this study is that 79% of the triatomines tested were collected via methods that allowed for the calculation of capture rates and therefore vector potential, the other 21% of triatomines, including all specimens of T. dispar and P. humeralis, were collected via convenience sampling, which may lead to sampling bias. Despite finding significant associations between habitat, collection method, and species, I was unable to further test any hypotheses as to whether species were more likely to be collected in particular habitats or by specific collection methods because of the sporadic nature of many of my collections. For several species I also had low sample sizes, likely a result of both rarity and unequal collection efforts across habitats and collection methods. While in some species I found high infection prevalences, it is not clear if sample prevalence is consistent with population-wide infection prevalence in these species. Studies that specifically target some of these rare species should be performed to characterize their infection prevalence and potential for contribution to human disease risk more definitively, and the use of various collection methods and surveys of many habitat types should be considered in future studies. Finally, for many of these species, basic natural history information is lacking, which makes a complete understanding of their potential as human disease vectors difficult. The vector potential presented in this study is a function of capture rates and infection prevalence, but does not take into account host preferences, defecation habits, or other qualities known to be important in determining vector capacity of triatomines [18,64,65]. Epidemiological importance of triatomine species is often linked to the degree of domestication [34], but this association may not be predictive if sylvatic species are transiently attracted to domiciles and have high infection prevalences. Recent studies have emphasized the importance of understanding the sylvatic cycle of Chagas disease [30,31] and there is an additional need for studies that focus on the natural history of sylvatic triatomine species to 35 understand their feeding preferences, defecation habits, sources of attraction, and potential for adaptation to human-altered environments. In this study, both ‘hand’ and ‘household’ collected triatomines were captured in or around human habitations, comprising a total of 86% of the bugs collected during this study. Although no triatomine species in Panama is considered fully domestic, R. pallescens and T. dimidiata are both common in the peridomestic environment, and P. geniculatus and rufotuberculatus have been shown to colonize peridomestic habitats in other regions [22,23]. I found high T. cruzi infection prevalence in all eight species tested in this study, and the differences in infection prevalence across species should prompt further investigation. Due to the changing ecology of Chagas disease transmission in Panama and other regions, more attention must be paid to the potential contribution of sylvatic triatomine species to the human transmission cycle, which may be an important aspect of reducing the human health burden of this neglected tropical disease.

Tables and Figures

Table 3.1. Number of triatomine species collected by sex, habitat, collection method, and location.

Total Collection Status Species Male Female collected Habitat Method Location Bur, Chil, Gam, LasP, hand (13), Rhodnius N(15), R(158), Llan, Cap, Cer, Esp, 238 109 128 house(176), pallescens S(44), U(21) Lsr, Nco, Nem, Sca, UV (49) Str hand(33), Panstrongylus Dar, Gam, Par, Llan, 42 26 15 N(39), R(3) house(4), geniculatus cer UV(4), Hg(1) hand(6), Panstrongylus Dar, Gam, Llan, Cer, 13 10 2 N(9), R(3), S(1) house(4), rufotuberculatus Lsr Hg(3) house(8), Triatoma dimidiata 9 3 6 R(9) Llan, Cer UV(1) hand(1), Eratyrus cuspidatus 6 3 2 R(2), S(3) Cer, Lsr house(5) hand(1), UV Triatoma dispar 4 2 2 N(4) (1) (others For unknown) Panstrongylus 2 0 2 N(2) hand (2) BCI, Gam humeralis

TOTAL 314 153 157 4 total 4 total 17 total

N = Natural habitat R = Rural, S= semi urban, U= urban | hand= convenience collected by hand, house= collected by household volunteers in or around their home, UV= collected at UV light or in UV trap, Hg = collected at mercury vapor light |Bur= Burunga, Chil= Chilibre, Gam= Gabmoa, LasP= Las Pavas, Llan = Llanito Verde, Cap = Capira, Cer= Cermeno, Esp= El Espino, Lsr= Llano de Santa Rosa, Nco= Nuevo Chorrillo, Nem= Nuevo Emperador, Sca= Santa Clara, Str= Santa Rita, Dar= Darien, Par= Paraiso, BCI= Barro Colorado Island, For= Fortuna

Table 3.2. Number of triatomines tested and T. cruzi infection prevalence (percent) by species and sex Number of bugs tested (proportion Species infected) M F 109 127 Rhodnius pallescens (87.2%) (33.1%) 25 15 Panstrongylus geniculatus (88.0%) (80.0%) 2 Panstrongylus rufotuberculatus 9 (88.9%) (50.0%) Triatoma dimidiata 2 (100%) 6 (100%) 2 Eratyrus cuspidatus 3 (33.3%) (50.0%) Triatoma dispar 2 (50.0%) 2 (0%) Panstrongylus humeralis 0 (NA) 1 (100%) 150 155 TOTAL (86.0%) (40.1%)

Table 3.3. Bloodmeal score by species and T. cruzi infection prevalence (percent)

Bloodmeal Score Species 1 2 3 4 5 Number of Bugs (Infection Prevalence)

R. pallescens 37 (59.5%) 77 (54.5%) 73 (61.6%) 33 (54.5%) 15 (60%)

P. geniculatus 8 (87.5%) 12 (83.3%) 15 (80%) 5 (100%) 0

P. rufotuberculatus 0 4 (50%) 5 (100%) 2(100%) 0 T. dimidiata 0 1 (100%) 7 (100%) 0 0 E. cuspidatus 0 2 (50%) 0 1 (0%) 2 (50%) T. dispar 0 1 (0%) 3 (33.3%) 0 0 P. humeralis 0 0 1 (100%) 0 0 104 17 TOTAL 45 (64.4%) 97 (57.7%) 41 (60.9%) (68.3%) (58.8%) Bloodmeal scores: 1= no blood present, guts desiccated or not present, 2= guts present but no trace of bloodmeal, 3= some small traces of bloodmeal present (dark specs in guts), 4= blood evident but not much or not fresh (dried or clotted blood), 5= blood evident and fresh or copious

Table 3.4. Multiple logistic regression Analysis of Deviance table showing highest likelihood model for T. cruzi infection prevalence Model Model Parameters LR Chisq df p value Species 24.45 6 <0.001 Habitat 2.16 3 0.54 Infection~ Species *Habitat + Sex Sex 70.09 1 <0.001 Species *Habitat 11.40 4 0.022

Figure 3.1. Trypanosoma cruzi infection prevalence by species and sex. Error bars indicate one standard error; N= total number of bugs tested. Triatomine photos by JM. Ayala Landa unless otherwise noted, used with permission.

Figure 3.2. Vector potential by species. Vector potential was calculated per species as the product of collection rate and T. cruzi infection prevalence, divided by the sum of the products of the collection rates and infection prevalence of all species, and represents a relative contribution to disease risk by species. Triatomine photos by JM. Ayala Landa,unless otherwise noted, used with permission.

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CHAPTER 4: ECO-EPIDEMIOLOGICAL SURVEY OF CHAGAS DISEASE RISK ACROSS LAND-USE GRADIENTS IN CENTRAL PANAMA

Abstract

Chagas disease is a neglected tropical disease that affects millions of people worldwide and is transmitted by multiple species of triatomine “kissing bug” (Hemiptera: Reduviidae). In Panama, species that transmit the disease are sylvatic, and the ecological and epidemiological risk factors that govern human transmission risk are not well characterized. In this study, I investigated the factors potentially contributing to Chagas disease exposure risk across human land-use gradients in central Panama. Epidemiological surveys and sixteen months of in-home triatomine collection were conducted in nine communities across three urban-to-rural land-use gradients. Household surveys examined social and behavioral factors, such as living conditions, education level, socioeconomic status, and knowledge of both triatomines and Chagas disease. Reported presence of domestic and wild animals around the home was positively correlated with observations of triatomines around the home and with greater average triatomine capture during the study. Household collections captured five triatomine species in and around homes, with a greater species richness of triatomines in more rural areas. Generalized linear mixed models revealed significantly more kissing bugs captured in semi-rural and rural areas as compared to urban areas. Additionally, the number of domestic animals, especially chickens, that households kept and the frequency and richness of wildlife reported around the home, were both significantly associated with the triatomine capture rate. PCR and qPCR testing indicated an overall Trypanosoma cruzi infection rate of 56%. In rural areas, 65% of triatomines were infected with T. cruzi, while in semi-rural and urban areas 57 and 51% were infected, respectively. Finally, the number of domestic chickens around the home was positively and significantly related to the overall density of infected triatomines around the home, and rural and semi-rural areas had significantly greater densities of infected triatomines compared to urban areas. These findings highlight disparities in Chagas disease transmission risk across land-use gradients and human demographic and household factors.

Introduction

Neglected Tropical Diseases (NTDs) affect over a billion people worldwide and cause an estimated 530,000 deaths each year [1,2]. Many NTDs are vector-borne and zoonotic and their 49 distributions and burdens are governed by complex interactions between numerous factors including: reservoir hosts, insect vectors, human hosts, the environment, and human socioeconomic and behavioral factors [3,4]. Yet historically, disciplines such as ecology and epidemiology have attempted to gain understanding of such complex disease systems primarily through discipline-specific approaches that may overlook important factors that contribute to exposure risk. The nascent field of eco-epidemiology combines such disciplinary approaches to understand how ecological and epidemiological factors interact to govern infectious disease dynamics [4,5]. Chagas disease is a prime example of a vector-borne disease that necessitates a broad understanding of the ecological and epidemiological risk factors that underlie transmission risk [6,7]. Throughout the Americas where Chagas disease is endemic, 5-10 million people are thought to be currently infected with the causative agent of Chagas disease, Trypanosoma cruzi, with an estimated 29,000 new cases annually [8–10]. Trypanosoma cruzi is a protozoan parasite transmitted in the feces of dozens of species of blood-feeding triatomine “kissing bugs” (Hemiptera: Reduviidae), also known colloquially as ‘chinches mamones’, ‘vinchucas’, or simply ‘chinches’[11,12]. Triatomines defecate immediately upon or shortly after bloodfeeding, and disease transmission occurs when parasite-laden feces enter the bloodstream via the bite wound or mucous membranes, or by fecal contamination of food sources [8,13]. Adding to the complexity of disease transmission, there are more than 150 wild and domestic mammals which can serve as competent reservoir hosts for T. cruzi throughout its range, and sylvatic species of triatomine maintain an enzootic cycle of infection, making complete eradication of Chagas disease infeasible [13–15]. In much of South America the primary vectors of Chagas disease, Triatoma infestans and to some extent Rhodnius prolixus, are ‘domestic’ in nature and complete the entirety of their life cycles within human dwellings. For this domestic transmission cycle, the major factors that govern risk of exposure to Chagas disease have been well-characterized [10,16,17]: extreme poverty, housing materials that provide habitat for triatomines (e.g., thatch roofs and mud walls), and proximity of domestic animals to human dwellings greatly increase infestation by domiciliary triatomine species [16,18]. In contrast, triatomine species in Panama and much of the rest of Central America generally are sylvatic in nature, at times congregating in the peridomestic environment [19–21]. Transmission to humans in these regions generally occurs at lower rates compared to areas with domestic transmission [8] and most likely takes place when non- domiciliary triatomine species are attracted to human dwellings, perhaps by bloodmeal hosts or light sources [19,22–26]. Owing to the nature of triatomine blood-feeding, which generally takes 50 between 10-30 minutes for full engorgement [27,28] and therefore likely takes place on sleeping hosts [29], the transmission of T. cruzi to humans by sylvatic vectors still is most likely to occur in the domestic environment. Panama is undergoing rapid anthropogenic land-use changes [30], including deforestation and urbanization, that can increase human exposure to disease vectors, including triatomines [31,32]. In these areas, the ecological and epidemiological factors governing human exposure risk are not as well understood [33]. Mean seroprevalence, a measure of the prevalence of the pathogen in human blood serum, of Chagas disease reported in humans in Panama is ~2-6% (22,27). However, in some areas seroprevalence rates of up to 30% have been reported in humans living in homes where kissing bugs have been collected (28). This great variation in infection prevalence indicates that transmission is not uniform across the landscape but may vary greatly both between and within communities. Changes in human land- use, including deforestation, can increase abundance of kissing bugs near the home, potentially increasing risk of T. cruzi transmission [31]. In Panama, the palm-dwelling triatomine Rhodnius pallescens is considered the most important vector of T. cruzi to humans [34,35]. However, at least six other species, including Triatoma dimidiata, which has peridomestic and domestic populations throughout its geographic range [36], and Panstrongylus geniculatus, which shows recent evidence of domiciliation in parts of South America [37,38], are potential sylvatic vectors of T. cruzi to humans in Panama [11,19]. At least five other species of triatomine in addition to R. pallescens have been captured in homes [39–41]. What factors attract sylvatic vectors to the domestic setting, and even which species of triatomine may be important vectors of T. cruzi in Panama, remain unknown. However, there is mounting evidence that anthropogenic land use changes influence the density and infection prevalence of the most common vector species in Panama, R. pallescens [31,42,43]. An improved understanding of the distribution, abundance and extent to which these triatomine species occur in homes and which epidemiological factors are predictive of occurrence is critical to understanding the sylvatic transmission cycle both in Panama and throughout the Americas where multiple sylvatic triatomine species co-occur. In this study, I employed an eco-epidemiological approach to explore the risk factors associated with household invasion by triatomine insects and their infection rates with T. cruzi, across human land-use gradients in central Panama. Data were collected using a community- based collection approach, a method that uses household volunteers to perform collections and has been found to be both efficient and cost-effective in monitoring household invasion (and reinvasion) of triatomines [44,45]. I combined this approach with an epidemiological survey to 51 examine household-level risk factors. Together, these approaches allowed me to determine important ecological and epidemiological factors that may contribute to exposure risk for Chagas disease by sylvatic triatomine species in Panama.

Methods

Site selection: Communities for combined ecological and epidemiological investigation of Chagas exposure risk were selected via Google Earth imagery (accessed in May 2015), land use cover maps from MiAmbiente (2012), and Panama 2010 census records to determine human land use and community population size. As a result, nine communities were identified along three roads spanning urban-to-rural land use gradients and each community was selected to represent one of three human land use categories: urban, semi-rural, and rural (Figure 4.1, Table 4.1). Transects were between 4.5 and 10.5 km long, and communities ranged in size from 560 persons in the smallest rural community to over 6,000 persons in the largest urban community. Communities were chosen based on habitat use and census classification, with all urban communities used in this study classified according to Panama census records as ‘urban’ and all semi-rural and rural communities used in this study classified as ‘rural’. Here, semi-rural communities were considered distinct from rural communities due to greater density of households and less agricultural or forest land use surrounding homes. House selection: Using Google Earth imagery, a circle of radius 800 m was drawn from the approximate center of each community and divided into quadrants. In each quadrant, all visible buildings were identified and numbered. From these, 10 buildings in each quadrant were randomly selected using a random number generator to be visited for potential inclusion in the study. Of the 400 buildings identified, 60 were not residential houses (e.g., churches, schools, office buildings), or were vacant houses. The remaining 340 houses were visited a minimum of three times to request participation in this study. A total of 217 households were successfully contacted and agreed to participate in the study in June-July, 2015. By the end of the study 35 houses had dropped out, leaving 182 houses that participated throughout the duration of the 16- month study, a total participation rate of 53% of the households originally contacted. Household visitation and triatomine collection: An adult family member in each household was given a pamphlet with information about how to identify triatomines, a description of Chagas disease and its transmission cycle, and the purpose of the study. Participants were given a urine collection container affixed to which were photographs of five common species of triatomine, a plastic zip top bag, a 5 ml vial filled with 95% ethanol, and 52 instructions on how to safely collect insects found in the home. A blank label was affixed to the 5 ml vials and participants were asked to note the date and location of collection (i.e., inside or outside the home); if vials became full, the participants were asked to store any additional triatomines in the freezer until researchers returned to collect them. Researchers returned a minimum of four times throughout the following 16 months to collect any insects captured. Whenever a participant collected an insect, researchers confirmed the date and location if known. Whenever an insect other than a triatomine was collected, researchers explained the morphological differences between the insect collected and triatomines to aid in future triatomine identification. Identification and molecular analysis: Collected insects were identified to family and a list was compiled of all non-triatomine insects that were collected by volunteers throughout the study to determine the families of insects most commonly mistaken for triatomines. Triatomine specimens were stored in 95% ethanol until molecular analysis for T. cruzi. Triatomines were sexed, measured (total body length and wing length) and identified to species per Lent and Wygodzinsky [11]. To determine T. cruzi infection status, triatomine guts were dissected and subjected to DNA extraction using Qiagen DNeasy Blood and Tissue Kit with an overnight lysis step. Samples were subjected to both PCR and qPCR analysis to determine infection status, and only specimens positive for T. cruzi by both protocols were considered infected for subsequent analysis (see Chapter 3 for a complete description of infection testing). Epidemiological surveys: In July-August of 2016, after approximately one year of household collections, participating households were asked to complete a questionnaire to identify ecological and epidemiological risk factors that may contribute to presence of triatomines in and around the home. All survey participants were over the age of 18 and asked to read and sign an informed consent form before participation. The questionnaire was read aloud to each participant in their native language (i.e., Spanish) with the help of a translator. This survey was approved by the Institutional Review Boards of the University of Illinois Urbana- Champaign, the Smithsonian Institution, and the Gorgas Memorial Institute in Panama. Survey questions included demographic information (e.g., age, income, ethnicity, education, number of individuals in household), knowledge of and experience with triatomines, knowledge of and experience with Chagas disease, and household information (e.g., housing materials, number of domestic animals, ‘wildlife score’: frequency and diversity of wildlife observed around the home) (Table 4.2). Participants were asked about all domestic animals kept, especially chickens and dogs, which have been shown in the domestic setting to be important determinants of household infestation with triatomines [16]. Participants were also asked about the frequency 53 with which they encountered specific mammalian wildlife species around the home. These species are noted in Table 4.2 and were selected based on previous studies in Panama that investigated potentially important reservoir species for T. cruzi and bloodmeal hosts for triatomines [42]. Additionally, participants were presented with five pairs of preserved insect specimens, each pair including one triatomine and one non-triatomine, and asked to identify which were triatomines. All insects used in this part of the survey had been collected previously by household volunteers, and I chose non-triatomine samples that were commonly confused with triatomines. Statistical analyses: All statistical analyses were conducted in R (version 3.4.3). First, a correlation matrix was compiled of Kendall-Tau correlations to determine what factors may influence the density of bugs (DOB) collected, the bug infection prevalence (BIP), and the density of infected bugs (DIB), using the psych and corrplot packages [46,47]. Based on the strength and ecological or epidemiological relevance of these correlations, independent variables were selected for inclusion in multiple generalized linear regression models to determine the important risk factors governing DOB, BIP, and DIB. To determine the risk factors associated with DOB, BIP, and DIB, mixed models were used in the lme4 and glmmTMB packages in R [48,49], including community as a random effect and the following fixed effects, chosen based on the results of initial Kendall-Tau correlations: land use, number of chickens kept (log+1 transformed), number of domestic dogs and cats kept, and wildlife score (i.e., a composite estimate encompassing the frequency and species richness of wildlife participants reported seeing around the home). In the initial DOB and DIB models, two household demographic variables were also included: monthly household income (log- transformed) and education level (primary, secondary, or university). Non-significant variables were removed sequentially and models were compared using AIC to determine the overall best fitting model. In the DOB model, initial distribution testing revealed a zero-inflated mixed model fit to a linear negative binomial (NB1) distribution [50] best fit the data. This distribution accounts for zero inflation and over-dispersion of count values and assumes variance increases linearly with the mean. The zero-inflated distribution was tested in relation to several demographic variables: monthly household income, education level, previous exposure to triatomines, how worried respondents were about being bitten by insects in their home, and ability of respondents to correctly identify a triatomine. To determine the risk factors associated with BIP, a logistic regression mixed model was used, and for DIB, it was determined that a negative binomial mixed model without zero inflation was the best fitting model. Finally, to examine the effect of

54 window screens on the proportion of bugs collected inside versus outside the home, odds ratios were computed using the epitools package [51].

Results

Across the nine communities, 182 households participated in triatomine collections for the 16 month duration of the study and responded to the household questionnaire. Respondents generally agreed to answer every survey question, with the exception of the question regarding monthly household income, which 24 households declined to answer. Participants collected 186 specimens of five different triatomine species during the 16 month period. Both abundance and species richness of triatomines in communities were highest in the rural portion and lowest in the urban portion of the human land use gradient (Figure 4.2). In the Kendall-Tau correlations, land use (urban vs semi-rural vs rural; tau=0.21, p<0.01), the total number of domestic animals kept (tau=0.18, p<0.01), as well as the number of chickens (tau=0.22, p<0.01), wildlife score (tau=0.22, p<0.01), and previous exposure to triatomines (i.e., a respondent reporting ‘yes’ to having ever seen a triatomine prior to the start of this study; tau=0.25, p<0.01) all were positively correlated with the number of triatomines collected throughout the study period. However, only wildlife score was significantly correlated with the overall infection prevalence of bugs, and the negative correlation implies the greater frequency and/or species richness of wildlife participants reported seeing around the home, the lower the triatomine infection prevalence (tau = -0.31, p<0.01; Figure 4.3). Regression models: After model selection, the best-fitting model to predict DOB included: land use, chickens, and wildlife score, as well as the number of dogs and cats kept. The number of dogs and cats was not a significant predictor but the model without dogs and cats would not converge due to small eigenvalues. Additionally, the best-fitting zero-inflation factor was whether a person reported seeing a triatomine in the past. Even though having seen a triatomine in the past was not a significant predictor of zero-inflation, a model which included this variable was a better-fitting model than a model with no zero-inflation or with constant zero- inflation, suggesting that zero-inflation was present though minimal. In this model, both semi- rural and rural areas generated significantly greater collection rates of triatomines than urban areas, and all variables were associated positively with the overall density of bugs collected (Table 4.4, Figure 4.4a). The best-fitting model to predict BIP included three variables: chickens, wildlife score, and land use, although the effect of land use was marginally significant (Table 4.5, Figure 4.4b). Wildlife score, reflective of frequency and/or diversity of wildlife 55 participants reported around the home, was negatively associated with infection prevalence, while the number of chickens kept by households was positively associated with infection prevalence. The model that best fit DIB included only land use and the number of chickens kept, and a negative binomial mixed model without zero-inflation was a better fit than with any of the tested zero-inflation factors. This additionally supports the conclusion that zero-inflation was limited in the dataset overall. Wildlife score was not a significant predictor of the density of infected bugs, possibly because it correlates negatively with infection prevalence (Table 4.5) and positively with the density of bugs (Table 4.4). However, both land use and the number of chickens were strong predictors for the density of infected bugs (Table 4.6, Figure 4.4c). Additional results: Absence of window screens was not significantly associated with the collection of triatomines inside the home (p= 0.07, Figure 4.5), either in the complete data set or when analyzed by land-use, which is likely due to the very small proportion of households that had screens on all windows (Table 4.3). Overall, 57 individual houses collected at least one triatomine throughout the course of the study, and an additional 61 houses collected only non- triatomine insects, while 64 houses collected no insects of any kind. The most commonly collected non-triatomines were other hemipterans, including insects in the families Coreidae (n=32), Pentatomidae (n=43), and non-triatomine Reduviids (n=47). An additional 126 coleopterans (most commonly, cerambycids and curculionids) also were collected by volunteers during this study (Table 4.7).

Discussion

In this study, I collected ecological and epidemiological data to understand the causes of variation across human land use gradients in the density of triatomines, their infection rates for T. cruzi, and the product of these two variables, the density of infected triatomines. In total, 186 triatomines of five different species were collected in 57 unique houses across three urban-to- rural land use gradients in central Panama, with a total mean infection prevalence for T. cruzi of 58%. The overall density of triatomines collected around the home was most strongly associated with human land use, wildlife score, and the number of chickens; the number of dogs and cats on the property were also included in the best fitting model, although the number of dogs and cats was not significantly associated with triatomine density. In this model, all predictors were positively associated with the density of triatomines, consistent with previous studies both in domestic settings in other countries [52] and across an anthropogenic land use gradient in Panama [32]. Studies of the domestic transmission cycle of Chagas disease have 56 indicated the presence of common domestic animals, both dogs and chickens, substantially increases the density of triatomines found in the home [52,53], as domestic animals serve as preferential bloodmeal sources compared to humans [54]. Additionally, studies across gradients of human disturbance have found higher densities of R. pallescens in more highly disturbed habitats [32], while other studies have found that several sylvatic triatomine species thrive in habitats that are of intermediate anthropogenic disturbance, rather than high anthropogenic disturbance (i.e., in rural as compared to urban areas) [55]. These previous findings support this study’s determination that the density of triatomines was higher in more rural areas. Rhodnius pallescens, the most commonly collected species in this study, has one of the broadest recorded host ranges [21,42], so the finding here that an increase in the frequency and/or diversity of wildlife reported around the home increases the density of bugs collected could be due to a greater availability of bloodmeal hosts supporting a larger population of bugs near the home. Although a model for DOB that included zero-inflation was the ‘best-fitting’ model according to AIC, based on the non-significance of the zero-inflation term and the similarities of estimates in a zero-inflated and non-zero-inflated model, it would appear there is very little zero- inflation to account for. Zero-inflated models assume that the large number of zeros can be the result of two processes: ‘true’ zeros and ‘false’ zeros [56]. In this system, out of 182 houses participating in the study, 125 collected no triatomines. While there is a good chance that in many cases (e.g., especially in urban areas) these zeros are a result of unsuitable habitat and triatomines truly not being present in these areas (i.e., ‘true’ zeroes), there are also plausible sources of ’false’ zeros. For example, some households may have participated more enthusiastically than others, or were otherwise more adept at finding and capturing triatomines. Unfortunately, in my data I do not have a metric to quantify household participation (although ~65% of participating households collected at least one arthropod throughout the study), so the use of a zero-inflated model helps account for any ‘false’ zeroes. I tested a number of demographic and household-specific variables (e.g., education, income, level of worry regarding Chagas disease, accuracy of triatomine identification) to determine the best distribution of the zero-inflation factor, and in the case of predicting the density of triatomines, whether a person reported ever having seen a triatomine prior to this study was the best predictor of zero-inflation, although as a predictor itself it was not significant. Thus, although there may be zero-inflation in this data set, it is likely very small, and the vast majority of zeros in this study are likely ‘true’ zeros and not the result of differential participation.

Infection prevalence was best predicted by a model that included land use, the number of chickens on the property (log+1 transformed), and the wildlife score. That land use was not a significant predictor was likely due to the very large variance in infection prevalence across the habitats (Figure 4.4b). While there have been few previous efforts to examine variation in T. cruzi prevalence across human land use gradients, other studies have found significantly greater infection prevalence in the peridomesitic environment as compared to forest patches and contiguous forest habitats [42]. Here, a high species richness and/or frequency of wildlife around the home was associated with lower infection prevalence, which in some ways is consistent with data that have shown greater infection in R. pallescens across more fragmented landscapes with a less diverse host assemblage [42]. However, chickens were found to be positively associated with infection prevalence, which is counter to previous studies that have found the presence of domestic chickens to decrease triatomine infection prevalence in the domestic setting [52]. In predicting the density of infected triatomines, only land use and the number of chickens on the property were included in the best fitting model. This model was best fit to a negative binomial distribution without zero inflation, further supporting that zero inflation was minimal in these data. Wildlife score was not significantly associated with the density of infected triatomines around the home, although it was a significant predictor in both the BIP and DOB models. This may be because the wildlife score (richness and frequency of wildlife reported around the home) was negatively associated with infection prevalence but positively associated with the density of triatomines, so the net effect on the density of infected triatomines may have been neutral. While this finding is consistent with aspects of previous studies [42], one limitation of this study is that the wildlife score cannot distinguish between frequency and species richness of wildlife around the home. For example, a home in a rural area may have a wildlife score of 6 because they reported seeing six different species ‘occasionally’ around the home, while another home may have the same wildlife score but have reported seeing only three species ‘frequently’ around the home. Further, these data are susceptible to recall bias as well as recognition bias, with participants in some areas or some households potentially more likely to see or report seeing wildlife around their home because they are more familiar with wildlife, work outside, spend more time outdoors, etc. The number of chickens kept per household was associated with an increase in both the density of bugs as well as T. cruzi infection prevalence in this study. While chickens are adequate bloodmeal sources for triatomines, they are not competent reservoirs for T. cruzi [57]. Previous studies have also found the presence of chickens to increase the density of bugs 58 around the home [52]. However, finding a positive correlation between chickens and infection prevalence was unexpected, as previous research into the domestic transmission cycle indicates that the presence of domestic chickens can decrease triatomine infection prevalence[52]. This positive association between the presence of chickens and triatomine infection prevalence could be explained by a correlation between land use and the number of chickens kept, which was highest in rural communities, although the variance inflation factor for chickens and land use was low, suggesting that the collinearity between chickens and land use was not too high to consider both as independent factors in the model. It has been proposed that the mechanism by which chickens can decrease infection prevalence is by diverting bites away from competent reservoir hosts [19,52]. However, in the case where sylvatic adult triatomines are attracted to the household environment, this mechanism may not be effective in reducing infection prevalence in the triatomine population. In this study, there were no other significant household-level predictors of the three main outcomes (DOB, BIP, DIB). Importantly, human land use was included in all of the best fitting models and may capture elements of risk factors that co-vary with human land use, though many of those factors were included in model selection. Thus, ecological factors, including the environment and wild animal life around the home, in addition to indirect socio-economic factors including the number of domestic animals around the home, rather than education or income directly, might be important in predicting Chagas disease exposure risk in Panama. It is important to note, however, that there was very little variation in the construction materials used in the households surveyed, which are often important predictors of household invasion for domestic triatomine species [9,58,59]. Specifically, in this study, all of the houses except one were constructed of cinder blocks walls, with tin roofs and concrete or tile floors (one house had wooden walls and a dirt floor, although the family was in the process of constructing a cinder block and concrete house on the property). The one factor of household construction which did vary was the presence of window screens, which were significantly less common in more rural areas (Figure 4.5, Table 4.3). However, I found no significant relationship between having window screens and capturing triatomines inside the house, a finding likely due to a very small sample size, as only ten houses in rural and semi-rural areas that caught any triatomines had screens on all of their windows, and, on average, 46% of all bugs were caught indoors (Figure 4.5). Further research into the association between window screens and the presence of triatomines inside the home should be explored, especially in light of the negative association between the presence of screens and the use of bednets found in this study (Figure 4.3). Nearby palm trees, especially Attalea spp. palms, along with palm thatch roofs, have been 59 implicated in household infestation of Rhodnius spp. in the past [32,43]. While these factors were not explicitly modeled in this study, Attlaea palms are ubiquitous across rural landscapes in central Panama [32], and thus likely contribute to the increase in overall density of triatomines in rural areas. Participants in this study were able to correctly identify triatomines, on average, 72% of the time when asked to discriminate between a triatomine and non-triatomine insect specimen. This ability to correctly identify triatomines did not vary across the land use gradient, despite the fact that people in rural areas were more likely to have reported seeing a triatomine in their lifetime (Figure 4.3). In addition to the 186 triatomines collected during the course of this study, nearly 300 non-triatomine insects were collected by participants. The majority of these non- triatomines were other hemipterans, which can easily be mistaken for triatomines, as well as coleopterans, many of which have similar color patterns to R. pallescens, the most commonly found triatomine in this study. Each household was given a brochure with photographs and information about how to identify and safely collect triatomines, and collection containers had additional pictures of five of the most common triatomine species in Panama. Further education efforts targeted at helping community members to correctly identify triatomines could be beneficial, especially since community-based collections have been shown to be a valid and cost-effective way to monitor house infestation [44,45]. Throughout the course of this study, in addition to collecting information on the ecological and epidemiological risk factors that contribute to household attraction of triatomines, I was able to perform outreach in these nine communities, because each time a non-triatomine was collected, I explained what type of insect the volunteer had collected, and how to differentiate between that insect and a triatomine in the future.

Conclusions

This study provides insight into the eco-epidemiology of Chagas disease in Panama. Previous studies, especially in domestic settings, have suggested that chickens may be a source of zooprophylaxis, diluting the infection prevalence of triatomines while simultaneously serving as a preferable bloodmeal source when compared to humans [52,60,61]. Other evidence suggests that chickens may serve as a source of zoopotentiation rather than zooprophylaxis, by increasing triatomine density while decreasing the feeding rate on humans [54]. While this study lacks bloodmeal data to determine from what hosts triatomines are feeding when they enter the domestic environment, I found that the number of chickens around the 60 home is positively associated with both the density and the infection prevalence of triatomines, suggesting that in a sylvatic-to-domestic transmission scenario, chickens may increase rather than decrease overall risk. Previous studies also have suggested that chicken coops or nests serve as suitable habitat for triatomines [62–64], which could contribute to the domiciliation of sylvatic triatomines, a trend that has been seen in parts of South America [37,58,65–67]. Overall, I found substantial differences in exposure risk across human land-use gradients, consistent with other research [31,68] and suggesting that public health officials should consider the consequences of the rapid land-use changes in Panama and elsewhere in Central and South America for Chagas disease risk mitigation. Although research into the ecology and epidemiology of Chagas disease in Panama has a rich history [39,61–63 and see 33] there are not currently active surveillance programs in Panama for assessing or monitoring Chagas disease cases in humans [33]. Previous studies have examined people’s knowledge of and experience with triatomines [39], and other studies have related household collections of triatomines to human or domestic dog seroprevalence [34,72–75]. However, these studies primarily focus on rural or remote areas and may underestimate risks across wider land-use gradients, including urban areas where transmission may also occur. My findings suggest that land use may be one of the most important factors in predicting Chagas disease risk in Panama, and further research is necessary to elucidate the roles of specific wildlife or domestic animals, along with the interactions between myriad ecological and epidemiological variables that determine variation in human exposure risk for vector-borne disease across land use gradients.

Tables and Figures

Table 4.1. Characteristics of study communities by transect and land use classification Census Number of % Land Cover within 1.5km buffer1 Land Use Community Transect Population houses Agricult/ Shubs/ Classification Forest Urban Size (2010) participating Pasture other Capira 1 3,432 Urban 21 16.0 29.3 52.2 2.5 Llano de 1 944 Semi Rural 19 Santa Rosa 25.9 9.1 65.1 0.0 Cermeño 1 962 Rural 20 46.2 8.5 39.9 5.4 El Espino 2 2,422 Urban 23 32.0 44.1 21.9 1.9 Santa Rita 2 1,417 Semi Rural 18 16.8 7.1 71.7 4.3 Llanito 2 560 Rural 26 Verde 14.7 0.6 79.0 5.7 Nuevo 3 6,189 Urban 15 Chorrillo 20.8 47.4 8.0 23.8 Nuevo 3 2,003 Semi Rural 18 Emperador 33.7 0.3 51.7 14.3 Santa Clara 3 1,653 Rural 22 39.0 17.7 26.3 17.0 1 Land use categories are based on 2012 Forest Cover and Land Use map produced by MiAmbiente. The category of ‘forest’ includes ‘secondary mixed broadleaf’, ‘mature broadleaf’ and ‘coniferous’ forest; ‘urban’ includes ‘urban areas’ and ‘infrastructure’, ‘agricult/pasture’ includes ‘pasture’ and ‘pineapple’, and ‘shrubs/other’ includes ‘herbaceous vegetation’ and ‘shrubs/bushes’.

Table 4.2 Epidemiological Survey Questions and Responses Question/ Item Answers Notes Demographics Age Continuous Gender 0 = male, 1= female Ethnicity 0 = mestizo, 1 = Spanish, 2= indigenous, 3 = Spanish and indigenous, 4 = African-American, 5 = Asian Highest level of Education, 1 = primary, 2= secondary, 3= university Monthly Income Continuous Income was log-transformed in analyses Number of individuals in the household Continuous Previous knowledge/exposure to triatomines/Chagas Have you ever heard of triatomines 1 =Yes, 0 =No, 2=Not sure “Not sure” grouped with prior to this study? “No” for analyses Were you previously aware triatomines 1 =Yes, 0 =No, 2=Not sure “Not sure” grouped with could spread disease? “No” for analyses Have you ever seen a triatomine in your 1 =Yes, 0 =No, 2=Not sure “Not sure” grouped with life prior to this study? “No” for analyses Have you ever seen a triatomine in or 1 =Yes, 0 =No, 2=Not sure “Not sure” grouped with around your house? “No” for analyses If yes, where in your house? 0= inside, 1= outside, 2 = both Can you identify a triatomine? 1= correct, 0 = incorrect (per pair) Score out of 100% (Participants were shown 5 pairs of insects and asked to identify which insect from each pair was a triatomine) Have you ever been bitten by a 1 =Yes, 0 =No, 2=Not sure “Not sure” grouped with triatomine? “No” for analyses How concerned are you about being 0 = not worried, 1 = not very, 2 bitten by insects in or around your =moderately, 3= very home? Have you heard of Chagas disease prior 1 =Yes, 0 =No, 2=Not sure “Not sure” grouped with to this study? “No” for analyses How concerned are you about 0 = not worried, 1 = not very, 2 contracting Chagas disease? =moderately, 3= very Have you ever been to the doctor 2 =Yes, 1 =No, 0=Never been bitten because you suspect you’ve been bitten by a triatomine? Have you or anyone in your family ever 1 =Yes, 0 =No, 2=Not sure “Not sure” grouped with been diagnosed with Chagas Disease? “No” for analyses Do you ever use chemicals to kill or 1 =Yes, 0 =No, 2= not sure “Not sure” grouped with repel insects in your house or on your “No” for analyses body? Clarification questions: what types do you use? How often do you sleep under a bed 0 = never, 1= sometimes, 2 = always Category of ‘sometimes’ net? includes only children sleeping under bednets

Table 4.2 Continued Animals How frequently do you see each of the following 0= never, 1 = Cumulative responses were animals around your home? [sloth, anteater, occasionally, 2= added to create the item opossum, bat, monkey, rat, armadillo, agouti, frequently ‘Wildlife Score’ squirrel, other]

Do you keep any of the following domestic Continuous # of # of chickens was log+1 animals? [Dogs, Cats, Chickens, Cattle, Goats, domestic animals kept transformed for analyses; Dogs Pigs, “other”] and cats were combined additively into a single variable How often do you allow your animals into the 0 = never, 1 = rarely, 2= home? + Clarification question: Which animals do sometimes, 3 = always you allow in the home? Housing materials/ Land Use Land Use Category 0= urban, 1 = semi, 2 = rural What is your roof/walls/doors/floors made of? Roof: 0 = metal, 1 = Question was free response but other collapsed into categories Walls: 0 = concrete blocks, 1= other Door: 0 = no screen, 1 = screen Floor: 0 = concrete, 1 = tile, 2= other Do you have screens on your windows? 2= screens on all Only homes with screens on all windows, 1 = screens on windows were considered to some windows, 0 = no have screens screens

Table 4.3 Summary statistics of bug density, infection prevalence, number of chickens, number of dogs and cats, and window screens by land use Land Use Urban Semi Rural TOTAL

Number of Houses 59 55 68 182 Participating

Total Number of Bugs 17 48 121 186

Mean T. cruzi Infection 0.51 0.57 0.65 0.58 Prevalence

Total Number of Infected 9 25 75 109 Bugs

Mean Density of 0.26 0.86 1.71 0.94 Bugs/House

Mean Density of Infected 0.14 0.45 1.05 0.55 Bugs/House

Mean Number of 2.36 9.21 8.15 6.57 Chickens Kept

Mean Number of Dogs 1.39 1.81 1.86 1.69 and Cats Kept

Mean proportion of houses keeping at least 0.24 0.47 0.55 0.42 one chicken Mean proportion of houses with screens on 0.49 0.25 0.19 0.31 all windows

Table 4.4. Summary of best fitting model results: zero-inflated negative binomial mixed model for density of bugs (DOB) (random effect: community) Conditional Model: Estimate Std Error Z Value Pr (>|z|) (intercept) -2.58 0.74 -3.50 <0.001 Land = Semi Rural 1.43 0.64 2.24 0.025 Land = Rural 1.92 0.62 3.10 0.002 Log(# of Chickens) 0.21 0.10 2.14 0.032 Wildlife Score 0.11 0.05 2.24 0.025 # of Dogs and Cats 0.06 0.07 0.85 0.392 Zero inflation model : (intercept) 0.39 0.80 0.48 0.628 Having ever seen a -20.17 7823.70 -0.003 0.998 triatomine

Table 4.5. Summary of best fitting model results: generalized logistic mixed model for infection prevalence (BIP) (random effect: community) Estimate Std Error Z Value Pr (>|z|) (intercept) 7.46 2.93 2.54 0.011 Land = Semi Rural -3.61 1.97 -1.83 0.066 Land = Rural -1.57 1.71 -0.92 0.359 Log(# of Chickens) 0.99 0.38 2.59 0.009 Wildlife Score -0.65 0.22 -2.88 0.004

Table 4.6. Summary of best fitting model results: negative binomial mixed model for density of infected bugs (DIB) (random effect: community) Conditional Model: Estimate Std Error Z Value Pr (>|z|) (intercept) -2.91 0.74 -3.96 <0.001 Land = Semi Rural 1.75 0.77 2.26 0.024 Land = Rural 2.43 0.75 3.24 0.001 Log(# of Chickens) 0.33 0.11 3.03 0.002

Table 4.7 Non-triatomines collected during household collections by order, family, and land use. Rural Semi Urban Total Insect Order and No. No. No. No. No. No. No. No. Family Arthropods Houses Arthropods Houses Arthropods Houses Arthropods Houses (Class) Arachnida/ (Order) Scorpiones Unidentified 0 0 1 1 1 1 2 2 Blattodea Unidentified 0 0 0 0 1 1 1 1 Coleoptera Cantharidae 0 0 2 2 0 0 2 2 Carabidae 0 0 0 0 1 1 1 1 Cerambycidae 13 12 20 15 24 12 57 39 Chrysomelidae 2 2 2 2 6 5 10 9 Curculionidae 4 4 11 8 11 5 26 17 Elateridae 0 0 0 0 1 1 1 1 Meloidae 3 2 2 2 3 3 8 7 Passalidae 0 0 0 0 1 1 1 1

Scarabeidae 1 1 2 2 2 1 5 4 Tenebrionidae 6 4 2 2 3 3 11 9 Unidentified 0 0 0 0 4 3 4 3 Diptera Culicidae 0 0 0 0 2 1 2 1 Hemiptera Belastomatidae 2 1 1 1 3 3 6 5 Coreidae 9 8 10 7 13 11 32 26 Largidae 2 2 0 0 0 0 2 2 Pentatomidae 13 9 13 8 17 12 43 29 Pentatomidae* 5 3 13 8 6 4 24 15 Reduviidae 20 14 19 12 8 4 47 30 Unidentified 2 2 3 3 3 3 8 8 Hymenoptera Formicidae 1 1 0 0 0 0 1 1 * some insects identified as belonging to family Pentatomidae may have been family Cydnidae

Figure 4.1 Map of Study sites. Black Squares = rural communities, Gray Circles = Semi-Rural Communities, White Triangles = Urban Communities. Land use map depicts agricultural and pastoral land cover in shades of yellow, forest in shades of green, and urban development in red. Map produced by MiAmbiente, 2012.

Figure 4.2. Total number of triatomines collected by household volunteers over 16 months by species and land use category.

Figure 4.3. Correlogram of 1X1 associations between covariates. Tr= "triatomine", prev = prevalence; ‘land use’ = Urban (0), Semi rural (1), Rural (2); education = primary school (1), secondary school (2), university (3); household size = number of individuals living in the household; ‘know of triatomine’ = have you ever heard of triatomines before this study (no/yes); ‘identify triatomine’ = proportion of the time (out of five trials) participants were able to correctly identify a triatomine when compared to another, non-triatomine insect specimen; ‘seen triatomine’ = have you ever seen a triatomine before this study (no/yes); ‘seen in home’ = have you ever seen a triatomine in or around your home (no/yes); ‘Heard of Chagas’ = have you ever heard of Chagas disease before this study (no/yes); ‘previously bitten’ = have you ever been bitten by a triatomine (no/yes/not sure); wildlife score= frequency and species richness of seven species of wildlife reported around the home (0-14); use of bednet= never (0), sometimes (1), always (2); window screens = screens on none (0) or all (1) windows

Figure 4.4. a) Mean density of triatomines (DOB, number of triatomines collected per house) b) Mean T. cruzi infection prevalence (BIP) and c) Mean density of infected triatomines (DIB, number of triatomines collected per house * infection prevalence) by land use, ± 1 standard error. Asterisks represent significant differences, see tables 4.4-4.6

Figure 4.5. Mean proportion of bugs collected inside the home (+/- 1 standard error), by land use and presence or absence of window screens. Due to small sample sizes, urban communities are not depicted.

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CHAPTER 5: A MATHEMATICAL MODEL OF HOUSEHOLD-LEVEL RISK OF CHAGAS DISEASE TRANSMISSION BY SYLVATIC TRIATOMINES

Abstract

Chagas disease, caused by a kinetoplastid parasite, Trypanosoma cruzi, transmitted in the feces of triatomine ‘kissing bugs’ (Hemiptera: Reduviidae), currently affects an estimated 5- 10 million people throughout the Americas. It has a disproportionate impact on the poorest communities and is one of the diseases targeted for reduction by the World Health Organization. Despite this impact, the factors governing the risk of disease transmission are not well understood. Several studies have modeled transmission risk in domestic settings, where triatomine insects persist in human dwellings for the duration of their life cycle. However, throughout their range, including in Panama, triatomines are transient visitors to human habitations, and thus the factors that govern attraction to the domestic environment and transmission risk require further study. This study uses a previously established model for T. cruzi, which estimates that transmission is dependent upon the per-contact transmission rate of the parasite and the number of potentially infectious contacts, following a binomial distribution. I use ecological and epidemiological data collected over a period of 16 months in central Panama to estimate parameter values for the number of potentially infectious contacts in households spanning an urban-to-rural land use gradient. While the overall per-person risk of infection is low across a land use gradient, risk and estimated infection incidence depend on the presence of domestic animals around the home and increase with rural land use. Further, the model predicts between 111-158 (± 153-220) new Chagas disease infections across Panama each year, with the majority of these infections likely occurring in rural or semi-rural environments. Specifically modeling the density of infected triatomines around the home gives the most accurate estimates of risk, but results are consistent between models when modeling the density of triatomines and assuming infection prevalence is constant in a given land use context.

Introduction

Chagas disease, a neglected tropical disease, currently affects 5-10 million people throughout the Americas [1,2]. The annual economic burden is an estimated $627 million in healthcare costs and that cost rises to $7.19 billion if disability-adjusted life-years (DALYs) are taken into account [3]. In Panama, it is estimated that about 1 in every 200 people (>18,000 79 individuals) in the country, are infected with Trypanosoma cruzi, the causative agent of Chagas disease [2]. These may be underestimates, as Chagas disease is not always tested for or reported, acute infection is often asymptomatic or presents with constitutional symptoms such that people may never seek medical care, and risk is heterogeneous across the landscape while case reporting is aggregated at coarse spatial scales [4,5]. To meet the World Health Organization’s (WHO) 2020 goal of interrupting intradomiciliary transmission [6], it is essential to understand what factors drive variation in transmission risk of Chagas disease. More than 15 genera of triatomine (Hemiptera: Reduviidae) transmit T. cruzi [2,7], but the majority of research focuses on Triatoma infestans and Rhodnius prolixus, the two most ‘domestic’ species of triatomines. In much of South America, the most common vector species, T. infestans, lives its entire life cycle within human dwellings [7], and several studies have modeled transmission dynamics in this environment [8,9]. Transmission of Chagas disease has been reduced dramatically in these well-studied areas, due to vector control, education, and treatment efforts by the Pan American Health Organization (PAHO) and the WHO [2,10]. However, in much of Central America, including Panama, the major vector species are considered peridomestic or sylvatic in nature [11] and the factors that contribute to transmission risk in these areas are less well-characterized. Thus, understanding of disease dynamics is limited, and effective control strategies remain elusive in many of these areas. While indoor residual spraying has been effective in reducing intradomiciliary populations of triatomines in much of South America [10,12,13], this strategy may not be effective in areas where triatomines do not nest within the home [14]. Even when eliminated from houses, sylvatic populations of domestic species can re-infest homes following insecticide treatment [15–18], so understanding how sylvatic populations of triatomines affect human disease risk is an important and understudied aspect of management and control of Chagas disease. While prevalence of Chagas disease is substantially lower both in Panama and in other communities where sylvatic or peridomestic triatomines dominate [19–23], transmission still occurs. Rhodnius pallescens, the most important vector species in Panama, while often considered a sylvatic species commonly found nesting in palm trees [7, 24], has a peridomestic distribution in many cases [11]. Other vector species in Panama, including Triatoma dimidiata and Panstrongylus geniculatus show varying levels of domiciliation [24]. Additionally, triatomines are attracted to lights at night such as those attached to the outside of houses, and the use of lights can increase risk of disease transmission in some areas [25–28]. Previous work in Panama has identified important factors leading to greater numbers of bugs collected around the home [20,29], but few models exist to predict how household characteristics may affect 80 attraction of sylvatic triatomines to the household environment and ultimately translate into risk of disease transmission [30].

Previous Models of Domestic Risk

The domestic cycle of Chagas disease is well-characterized [8,9]. Several mathematical models demonstrate the importance of domestic animals, specifically dogs and chickens, in increasing the density of infected triatomines around the home, which correlates to risk of infection [31–33]. Research based on homes in Argentina and other countries where T. infestans is the dominant vector has shown that households with the highest risk typically consist a family of five humans living with an average of two dogs and several chickens [9]. Further models incorporate the impact of various vector control scenarios on the risk for disease transmission to examine ways in which the WHO’s 2020 goals can be met [8,34]. However, these models still focus on a domestic transmission cycle, modeling household populations of triatomines in relation to the domestic animal and human composition of the household. Other models consider the sylvatic transmission cycle, but the majority of these are ecological niche models used to predict the distribution of triatomines across landscapes [35,36], and are useful for understanding spatial variation in disease risk but do not directly address risk factors for human transmission. At least one model, and several field studies, have demonstrated that proximity of houses to palm trees can directly influence the number of sylvatic species, especially Rhodnius spp., found around the home [37–39]. To predict variation in infection prevalence more precisely across heterogeneous landscapes, a more specific measure of transmission risk should be modeled. A number of studies estimate a per-contact transmission rate for T. cruzi, which can be used in combination with the estimated number of potentially infectious contacts over a given time period to predict the probability of infection for any given member of a household using a binomial law distribution [40–43]. The first challenge in systems where the majority of triatomines are sylvatic in nature and thus attracted only transiently to the domestic environment is in predicting the number of triatomines present on any given day, which is necessary to estimate the number of potentially infectious contacts over a given period of time. Here, I present a mathematical model of T. cruzi transmission risk to humans by triatomines attracted to the peridomestic environment across an urban-to-rural land use gradient in central Panama. To estimate household attraction, I incorporate household characteristics such as number and type of domestic animals kept, community land use structure (i.e.: urban, rural, or semi-rural), and frequency and species 81 richness of wildlife seen around the home. I build upon a model previously proposed in the literature to estimate probability of transmission [43], and parameterize the model using epidemiological and ecological data collected during a sixteen-month period in central Panama [Chapter 4].

Modeling Peridomestic Risk

The probability of transmission of T. cruzi to a susceptible person, r, in a specific household, j, over a given time, t, will follow a binomial law distribution:

푪풋푡 푟푗,푡 = 1 − (1 − 푇) (1)

Where T represents the per contact transmission rate and Cj represents the number of potentially infectious contacts a single person has per day in a specific household. Mathematical models validated with epidemiological data on the force of infection have predicted the per contact transmission rate (T) as 5.8X10-4 with a 95% confidence interval of 2.6X10-4 to 11.0X10-4 [43]. In this study, I tested the mean value of T as well as the 95% confidence interval values to generate a range of estimates for risk. The number of potentially infectious contacts per person per day is determined as the number of vectors present, Nvj, the infection prevalence of the vectors, Pvj, the biting rate of the vectors per day, bj, the fraction of total bloodmeals taken from humans, Fj, whether vectors were collected inside, Qij, or outside,

Qoj, the home, and the number of humans in the household, Nhj :

(푁푣푗푄표푗퐹푗)(푁푣푗푄푖푗)푃푣푗푏푗 퐶푗 = 푁ℎ푗 (2)

In this equation, Nh was set as 4, the average number of household members in this study area in Panama, and which did not vary across the human land use gradient (E.A.U., unpublished data). The biting rate of the vectors (number of bites per insect per day), b, is not well characterized; in previous studies it has been estimated to be 0.2, equivalent to one bloodmeal taken every 5 days, based on a single study [43]. Catala [44] used the proportion of fed vectors as a proxy for 24 hour feeding rate of T. infestans and found that it varies based on temperature, from 0.012 to 0.6, though this study was conducted where temperatures ranged from 10°C to 29°C. Another study found similar seasonal patterns, but with differences in the proportion of fed vectors, ranging from 0.3 to 0.6, corresponding to feeding rates of once per 1.7 days to 3.4 days, with the highest rates of feeding in the spring and summer months [45]. Still another found feeding rates of 0.14 to 0.34 corresponding to feeding frequencies of every 2.9 to 82

7 days [46]. None of these studies included the species most common in Panama, and laboratory rearing of triatomines has demonstrated that adults can withstand intervals of 2-3 weeks or more between bloodmeals [47]. Because of this variation, and because temperatures in Central Panama rarely fall below 20°C, in this model bj is tested at four values: 0.1, 0.2, 0.45, and 0.7. The proportion of vectors infected in this study, Pvj, was based on molecular testing of collected triatomines (Chapter 4) and set to the average prevalence per land use type: 51% in urban areas, 57% in semi-rural areas, and 65% in rural areas. All values used for parameter estimates and sources are shown in Table 5.1. In this study, I generated a more accurate estimate of the presence of infectious vectors by first modeling the density of infected bugs (NIvj) instead of the number of vectors around the home as the following:

(푁퐼푣푗푄표푗퐹푗) (푁퐼푣푗푄푖푗)푏푗 퐶푗 = (3) 푁ℎ푗 This avoids an additional assumption of a constant infection prevalence in a given land use context (Pv), which in this case can result in a more accurate assessment of the number of potentially infectious contacts based on household characteristics. We present the results of both models in this study, since in some cases for which infection testing could not be performed, equation 2 provides an adequate model of the number of potentially infectious contacts.

The fraction of bugs feeding on humans, Fj, depends on the number of humans in the household Nhj, as well as the total availability of vertebrate hosts, including the number of domestic animals in and around the home [9,31,33]. Dogs (Dj) and chickens (Bj) have been shown to be more attractive as a bloodmeal source than are humans, so the abundances of these two types of bloodmeal sources are multiplied by a ‘relative attractiveness’ factor, R. Based on previous studies in the domestic environment, dogs and chickens are estimated to be on average approximately three times as attractive to triatomines as are humans [9]. With this, the fraction of bugs feeding on humans in households that vary in the abundance of alternative bloodmeal sources can be calculated as:

푁ℎ푗 퐹 = 푗 푁 + 푅(퐷 + 퐵 ) ℎ푗 푗 푗 (4)

In equation 2, Nvj represents the number of vectors present in a house on any given day, while in equation 3, NIvj represents the number of infected vectors present in a house on any given day. In Panama, unlike in other systems where triatomines complete their lifecycles in the home, these numbers likely will be small and will depend on various household factors that 83 attract bugs from the sylvatic environment to homes. In this study I used the number of bugs collected (Ncj) in any given house to generate estimates for Nvj and NIvj.

To obtain estimates for Ncj, household collections of triatomines were conducted with the help of volunteers, from 182 houses in nine communities spanning three urban-to-rural land use gradients in Panama Oeste province, Panama (see Chapter 4 for full description). These collections spanned a period of approximately sixteen months and consisted of household volunteers capturing triatomines both inside and outside the home in a plastic urine collection container. Specimens were then stored in 95% ethanol and transported to the University of Illinois at Urbana-Champaign for identification [7] and pathogen testing [48]. Additionally, epidemiological surveys were conducted in each household to determine differences in household factors that may contribute to disease risk, including residents’ knowledge of and experience with triatomines, ability to correctly identify triatomines, and number and type of domestic animals kept. These data were analyzed using three different statistical models to predict either the density of triatomines or the density of infected triatomines. Model 1: to predict the density of triatomines around the home, I fit a generalized linear mixed model to a zero-inflated negative binomial (NB1) distribution with four covariates—the number of dogs, the number of chickens (log+1 transformed), the land use type, and the reported wildlife score, a number representing both the frequency and species richness of wildlife around the home, with prior encounters with triatomine insects as the zero-inflation factor. Model 2: a second model to predict the density of triatomines around the home, I fit a generalized linear mixed model to a negative binomial distribution without zero-inflation that included three covariates—the number of chickens (log+1 transformed), the land use type, and the wildlife score. Model 3: to predict the density of infected triatomines, I fit a generalized linear mixed model to a negative binomial distribution with two covariates – number of chickens (log+1 transformed) and land use. Using the generated estimates from these three models (Table 5.2),

I then estimated a value, Ncj (or NIcj in the case of Model 3), which represents the number of bugs collected in any given house, j, over the 16 month collection period. In this study, values were calculated for every combination of dogs (0-4), chickens (0-25), land use (urban, semi- urban, and rural), and wildlife score (0-14) observed in the field study. I then calculated Nvj, the number of vectors (or infected vectors, NIvj) present on any given day, as:

푁푐푗 푁퐼푐푗 푁 = 표푟 푁퐼 = 푣푗 푑 ∗ 푓 푣푗 푑 ∗ 푓 (5) Where d represents the number of collection days, in this case, 510, and f represents the fraction of bugs present in each house that were actually collected. This latter value is included 84 based on the assumption that 1) not every house participated equally in collecting bugs, and 2) not every bug that entered the house was likely collected. This assumption is reasonable considering that these are nocturnal insects [49,50] and could enter homes after inhabitants are asleep. Additionally, previous studies in Panama have found poor or very poor knowledge of Chagas disease risk [29]. One study in which trained entomologists performed searches of houses in a rural area in Panama found 69 triatomines in 20 houses after searching 67 houses in total [20]. However, it is not reported over what time period these searches took place, so this information is only partially useful in estimating a value for f. Of the participants in this study, there was variation in the ability to correctly identify a kissing bug as compared to other common species of flying insects, which could contribute to variation in collection success (E.A.U., unpublished data). Therefore, here I tested four values of f: 0.9, (i.e., assuming 90% of bugs that entered the houses during the study period were collected), 0.5, 0.2, and 0.1. The smaller the value of f, the larger the overall risk should be, as this would inflate the estimate of the number of vectors present in the home. Finally, I assumed that there is a difference in proportion of bugs likely to feed on humans depending on whether a bug was collected inside versus outside the home. In my study, an average of 49% of bugs collected were found inside the home. Unlike previous studies in Argentina where domestic animals are present within the home and therefore capable of ‘diverting’ bites away from humans [9,33], the majority (75%) of participants who kept animals in this study did not allow any domestic animals inside the home. I therefore assumed that of the bugs present in the domestic environment, Nvj, a fraction, Qij , of them would be inside the home and therefore feed entirely on humans, whereas the remaining fraction, Qoj , would be outside the home and would feed on humans only a fraction, Fj, of the time proportional to the attractiveness and availability of other hosts. Substituting the values generated in equations (4) and (5) back into equations (2) and/or (3) gave me an estimated number of potentially infectious contacts per day in each household. I then substituted these values back into the original equation (1) and calculated the probability of transmission for a single person in a particular household over any given period of time. I estimated the risk over a 12-month and 10-year period, as incidence of infection is often estimated over 12 months, and due to the low anticipated risk, long-term estimates can be useful in assessing per-person risk. I then used this risk estimate and the known population sizes of the communities studied based on Panamanian 2010 Census records [51] to estimate country-wide incidence of infection in one year, and compare this to the reported estimates of incidence of Chagas disease infection in Panama. I present here the results from these three 85 model exercises, which predict the overall per-person risk and yearly incidence of Chagas disease based on the estimates from three statistical models.

Results

The overall probability of transmission was calculated across a 12-month and a 10-year interval using three statistical models. In all models, risk is highest in rural areas compared to semi-rural and urban areas. Across all models, the mean predicted yearly incidence of infection across Panama is between 111 to 158 new infections, with standard deviations ranging from 153 to 220 (Table 5.3). Predicted incidence of infection varied greatly with changing values of f, the fraction of bugs collected, and b, the biting rate of the vectors, and varied across models (Figure 5.1). In all models, the number of triatomines present increases with an increasing abundance of domestic animals and is highest in rural areas as compared to urban and semi-rural areas, but the numbers of vectors estimated to be present on any given day is quite low (Figure 5.2). Importantly, Models 1 and 2 (Figure 5.2a, 4.2b) estimated higher densities of triatomines compared to model 3 (Figure 5.2c), but all the triatomines in model 3 are considered infected, whereas only a portion Pvj of the triatomines in models 1 and 2 are considered infected. In this study, I estimate that model 3, which specifically models the densities of infected triatomines around homes, is a more accurate model for predicting risk. Comparing statistical estimates of the Ncj and NIcj indicates that directly modeling the densities of infected triatomines as in model 3 results in the highest predicted incidence compared to modeling the density of triatomines and assuming a fixed infection prevalence with or without zero-inflation (Figure 5.1). All models predicted that transmission risk is highest in rural areas as compared to semi- urban and urban areas, which is consistent with both collections of triatomines in these areas as well as the estimated number of vectors present across the land use gradient. In all models, risk declines slightly in homes with very few animals, compared to homes with no domestic animals, then increases again with the number of domestic animals kept (Figure 5.3).

Discussion

I estimated the per-person risk of Chagas disease transmission across a human land use gradient over time and used these estimates to predict incidence of infection in Panama. Although the overall per-person risk of infection, even over long time scales, is low, this per- 86 person risk still predicts hundreds of potential new infections across the country each year and is consistent with currently estimated rates of new Chagas infections in Panama [2]. I used three different statistical models to predict the density of triatomines in households, Nvj and NIvj. An important difference between these is that models 1 and 2 estimate the total number of triatomines present and assume a fractional infection of 50-65% depending on land use (Table 5.1), whereas model 3 estimates the total number of infected triatomines, resulting in lower numbers of insects predicted but higher overall risk estimates. Models 1 and 2 also incorporate more parameters in the estimation of the number of triatomines collected than model 3, which includes only the land use type and number of domestic chickens. As model 3 accounts for the variation in infection prevalence based on specific household characteristics, it likely provides the most accurate prediction of risk. However, in some studies it may not be possible to test collected triatomines for infection prevalence, so assuming a static infection prevalence in a given land use context (as in models 1 and 2) may be a viable alternative. Overall differences in risk and incidence estimates between the three models were small, and the patterns in the results were similar, indicating models 1 and 2 may be adequate estimates of transmission risk for some scenarios. The very low per-person risk estimates predicted in this study support previous observations that Chagas disease prevalence is low in Panama as compared to other countries with a greater burden of domestic triatomine vectors [2,52]. However, human seroprevalence in Panama is understudied and estimates vary widely depending on the community in which samples are taken [20,21,53]. At least one study found seroprevalence of T. cruzi infection in humans as high as 23% [20], while studies from other areas suggest a much lower prevalence, typically 2-3% [21,53] or 5%[19]. This model does estimate heterogeneous risk depending on the location of households in the landscape and the composition of animals and humans within the household, consistent with many other studies that find heterogenous risk of vector-borne disease across gradients in human land use [e.g., 51]. Estimates by the World Health Organization predict approximately 175 new cases of Chagas disease in Panama each year [2], although it is not possible to verify these estimates against epidemiological data due to a lack of a mandatory reporting system [55]. My study predicted slightly lower incidences on average (i.e., 111-158 new infections/year, Table 5.3), but it highlights the importance of variation in incidence across the land-use gradient. Furthermore, the household surveys used to generate predicted numbers of household triatomines in this study were limited to central Panama, in nine communities specifically chosen because of their similarities. Risk may be higher in other regions of Panama, especially in communities closer to forested habitats, such as in higher 87 elevation areas, and better estimates of the numbers of triatomines present in homes in these areas would help to more accurately parameterize this model at the national level. However, the consistency seen in this model in predicting incidence across the three statistical models, and reasonable standard deviations despite varying values of T, f, and b, suggests this model is robust. While the availability of various domestic animals as bloodmeal sources affects the frequency of feeding on humans [31,33], in some cases these animals can also serve as sources of T. cruzi transmission to bugs [32] which could, in turn, affect the infection prevalence in vectors. Several studies using bloodmeal analysis have found anywhere from 25 to 68% of triatomines collected from the peridomestic environment in Panama had fed on humans, as compared to less than 2% having fed on dogs and chickens [20,56]. However, in these previous studies, the number of domestic animals kept in the home was not reported. In this study, all of the triatomines captured and tested for T. cruzi were first graded for bloodmeal score (Chapter 2). Of these, 47% were completely starved, 31% had evidence of a bloodmeal that was not fresh, and 22% had a very fresh bloodmeal in their gut at the time of capture. This information could be interpreted in several ways: 1) 47% of the bugs may have been flying towards houses in search of a bloodmeal, 2) 22% of the bugs may have taken a fresh bloodmeal in the peridomestic environment before capture, or 3) some combination of these two scenarios. In this model, I assumed that approximately half of the bugs would be inside the home and therefore feeding on humans, while the other half would be feeding on humans at a fractional rate of Fj, which decreases exponentially with the number of domestic animals kept outside the home. These estimates of the fraction of bloodmeals taken from humans may be overestimates, for bugs captured inside, and underestimates, for bugs captured outside, of the true fraction of bloodmeals taken from humans, and a future bloodmeal analysis of the bugs collected in this study could provide a better indication of the proportion of bugs feeding on humans across households. Additionally, biting rate of vectors, b, in this model was varied from 0.1 to 0.7, indicating 10-70% of triatomines present around the home on any given day would be taking a bloodmeal. As shown in Figure 5.1, varying this parameter can drastically change estimates of risk and therefore disease incidence, and a better understanding of the feeding dynamics of sylvatic species is necessary to better parameterize this model. While this model is useful to understanding how the factors that affect the abundance of triatomines around the home contribute to the overall infection risk, particularly with respect to land use type, there are limitations. First, the value of T, the per-contact transmission rate, was estimated in previous literature for T. infestans using mathematical modeling validated with 88 epidemiological data on the force of infection in several communities [43]. However, it is unclear if this per-contact transmission rate is consistent across other triatomine species. Successful transmission of T. cruzi requires that the feces of the infected insect come into contact with mucous membranes or open wounds of the host, allowing the pathogen to enter into the bloodstream to complete its lifecycle [57]. So, the pattern of feeding and timing of defecation of the triatomine is important in modeling transmission risk of Chagas disease. These patterns vary based on the triatomine species [58], and for decades this has been an important factor to consider when estimating risk of infection for humans [59]. Yet the primary vector species in Panama, R. pallescens, has been neglected in this regard, with only one study estimating that R. pallescens is a ‘tardy defecator’ as compared to R. prolixius; what effect this may have on the per-contact transmission rate of T. cruzi has not been characterized [60]. In this study, I included the mean estimated value as well as the 95% confidence interval extremes [43] in order to account for potential variation in per-contact transmission rate. This model also does not account for the possibility of infected humans within the household and assumes all household members are susceptible. Additionally, I do not consider a scenario in which domestic animals could infect triatomines, although studies have shown domestic dogs in Panama to be infected at rates varying from 11-17% [39,61]. Here, Models 1 and 2 assume a static triatomine infection prevalence in each land-use category based on molecular studies of the bugs captured around the homes (Table 5.1). Model 3, on the other hand, only accounts for the density of infected triatomines around the home and does not take into account the possibility that uninfected triatomines could become infected in the peridomestic environment. Importantly, R. pallescens and P. geniculatus inhabit chicken coops and pigsties, so it is possible triatomines are living within the peridomestic environment, even if not within the homes themselves [60,62]. This could have an important impact on density of triatomines outside the home and potentially decrease estimates of f, the fraction of bugs in the home environment that participants successfully collected during the study. As shown in Figure 5.1, varying this estimated fraction can have dramatic impacts on predicted disease incidence. Further, domiciliation of these sylvatic triatomine species is possible and in some cases appears to be increasing in frequency [63–65] which could, in the future, increase the number of vectors estimated to be present around homes. The presence of palm trees, especially Attalea spp., is an important risk factor for Chagas disease in areas such as Panama where Rhodnius spp. are the most abundant vectors, because palms crowns are the preferred habitat for this genus [7,38,66,67]. Further, a modeling study estimated an increase in transmission risk for humans when palms and houses are close 89 together [37]. While the presence of palm trees was not modeled explicitly in this study, land use was an important predictor of the number of vectors present as well as the infection prevalence; in this way, habitat surrounding homes was modeled implicitly. Modeling and field studies also demonstrate that the presence of lights increases overall risk of disease [25–28], which was not directly modeled in this study. However, all the houses included in this study had at least one light on the outside of the house, and future studies should take into account the presence or type of lights surrounding homes in estimating a value for the number of vectors present in the home. Seasonality can affect also the dynamics of infectious disease, including vector-borne diseases [68], but is not modeled here. Triatomine species, including those commonly collected in Panama, have shown seasonal differences in flight behavior [69], with more bugs collected during the rainy season as compared to the dry season in some cases, and the opposite pattern found in other cases [60,70]. Seasonal variations in temperature also have sometimes dramatic effects on the biting rate of triatomines [44,45]. It is imperative to understand these seasonal and temperature impacts on disease risk in the face of ongoing climate change, which is expected to have an impact on the distribution of vector-borne disease. Chagas disease specifically could be affected in a number of ways, from changes in rainfall affecting triatomine survival or activity [71,72], or in some cases changing the distribution of hosts they are seeking and how likely they are to infest homes [73]. The models described here are sufficiently general to be applied to any land use or specific household scenario, in order to estimate the probability of transmission of Chagas disease to an individual in a specific household. Using the density of infected triatomines around the home may result in more accurate model predictions, but assuming a constant infection prevalence and modeling the density of triatomines around the home can result in similar risk predictions when infection data are unavailable. This model, inclusive of variation in estimated biting rate, fraction of bugs collected in homes, and per-contact transmission rate, and parameterized based on three potential statistical models, shows considerable robustness and results in predictions in line with the WHO’s estimates of Chagas disease incidence in Panama [2]. Additionally, this model is able to predict incidence of infection at a land-use rather than a country-wide scale as with WHO predictions. Further research on biting rates and frequency of feeding on humans will be useful to assure the validity of this model and to apply it to a diversity of triatomine species. However, even when based on general parameters, such models serve an important role in improving understanding of disease risk across heterogeneous landscapes,

90 which will be essential to achieving the WHO’s 2020 goal of interrupting intra-domiciliary transmission throughout the range of Chagas disease [6].

Tables and Figures

Table 5.1. Parameters and estimates used in mathematical model Parameter Definition Value Source Proportion of vectors inside (i) Qo/ Qi Qi = 0.49, Qo =0.51 Chapter 3 or outside (o) the house Fraction of bugs in the home f 0.1, 0.2, 0.5, 0.9 estimates collected during the study b Biting rate of vectors 0.1, 0.2, 0.45, 0.7 [44–46] Number of humans in the Nh 4 Ch 3 household Number of dogs belonging to D 0-4 Ch 3 the household Number of chickens belonging B 0-25 Ch 3 to the household Number of vectors present at Calculated based on Models Nv Table5.2 any given time 1 and 2 and equation 5 Number of infected vectors Calculated based on Model NIv Table 5.2 present at any given time 3 and equation 5 Proportion of vectors infected Urban: 51.1%, Semi-rural: Pv Chapter 3 with T. cruzi 56.9%; Rural: 64.7% Per-contact transmission rate of 5.8X10-4 (±2.6 X10-4 – 11.0 T [43] T. cruzi X10-4)

Table 5.2. Statistical estimates used to predict Ncj or NIcj, respectively

Model 1: Ncj = exp(Intercept + Land use + log(Nchickens) + Ndogs + Wildlife )*(1- ZIF) Parameter Estimate Std Error Intercept -1.88 0.58 Land = Semi 0.84 0.48 Land = Rural 1.34 0.46 Log(Number of Chickens) 0.17 0.09 Number of Dogs 0.05 0.07 Wildlife 0.1 0.04 ZIF Intercept 0.94 0.69 ZIF Having Seen a bug -20.4 7192.63

Model 2: Ncj = exp(Intercept + Land use + log(Nchickens) + Wildlife ) Intercept -2.99 0.71 Land= Semi 1.69 0.64 Land = Rural 2.16 0.62 Log(Number of Chickens) 0.21 0.1 Wildlife 0.13 0.05

Model 3: NIcj = exp(Intercept + Land use + log(Nchickens)) Intercept -2.91 0.74 Land = Semi 1.75 0.77 Land = Rural 2.43 0.75 Log(Number of Chickens) 0.33 0.11

Table 5.3. Estimated Yearly Chagas Disease Incidence in Panama and number of potentially infectious contacts per person per year (Cj) MODEL 1: Nvj, using ZIF

Estimated Mean Estimated Cj ± LAND Yearly Incidence ± Standard Dev Standard Dev

Urban 24 ±33 0.02 ±0.01 Semi 31 ±42 0.08 ±0.06 Rural 56 ±78 0.15 ±0.11 TOTAL 111 ±153 0.25 ±0.18 MODEL 2: Nvj, no ZIF Urban 23 ±33 0.02 ±0.02 Semi 38 ±53 0.1 ±0.11 Rural 69 ±96 0.18 ±0.21 TOTAL 130 ±182 0.30 ±0.34 MODEL 3: NIvj, no ZIF Urban 28 ±39 0.02 ±0.02 Semi 44 ±61 0.11 ±0.13 Rural 86 ±120 0.22 ±0.26 TOTAL 158 ±220 0.35 ±0.41

Figure 5.1. Predicted yearly incidence of Chagas infection in Panama by land use type, vector biting rate (b) and estimated fraction of bugs collected (f), based on mathematical model of risk and three statistical model estimates of collection rates: a) Model 1, estimating density of bugs collected and including zero inflation, b) Model 2, estimating density of bugs collected not including zero inflation, and c) Model 3, estimating density of infected bugs collected, not including zero inflation. Each point is a mean estimate based on varying household parameters (number of chickens, wildlife score, etc.). Points are estimates based on a per-contact transmission rate (T) of 5.8 X 10-4, error bars represent 95% confidence interval estimates of T = 2.6 and 11.0 X10-4, respectively.

Figure 5.2. Predicted number of bugs present in a given household, by number of domestic animals present and land use: a) Model 1, estimating density of bugs collected and including zero inflation, b) Model 2, estimating density of bugs collected not including zero inflation, and c) Model 3, estimating density of infected bugs collected, not including zero inflation. Points are mean estimates across varied values of parameters f, b, and T, error bars represent one standard deviation.

Figure 5.3. Per-person risk of infection over 10 years by land use and number of domestic animals kept. a) Model 1, estimating density of bugs collected and including zero inflation, b) Model 2, estimating density of bugs collected not including zero inflation, c) Model 3, estimating density of infected bugs collected, not including zero inflation. Points are mean estimates across varied values of parameters f, b, and T, error bars represent one standard deviation. 96

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