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Arthropod-borne viruses, arboviruses, have the ability to replicate in both vertebrates and invertebrates and are transmitted to susceptible vertebrate hosts by vectors such as mosquitoes and ticks. Ticks are important vectors of many highly pathogenic arboviruses, including the flavivirus tick-borne encephalitis virus (TBEV) and the nairovirus Crimean-Congo haemorrhagic fever virus. In contrast, alphaviruses are principally mosquito-borne and have been isolated only rarely from ticks; ticks have not been implicated as their vectors. Nevertheless, the alphavirus Semliki Forest virus (SFV) replicates in cell lines derived from many different tick species, including those of the genus Ixodes, which includes vectors of TBEV and its lesspathogenic relative Langat virus (LGTV). In vertebrate cells, arboviruses generally cause cytopathic effects; however, arbovirus infection of arthropod cells usually results in a persistent low-level infection without cell death. While little is known about antiviral immunity in tick cells, the immune system of other arbovirus vectors such as mosquitoes has been studied extensively over the last decade. In insects, pathways such as RNA interference (RNAi), JAK/STAT, Toll, Imd and melanisation have been implicated in controlling arbovirus infection, with RNAi being considered the most important antiviral mechanism. In tick cells, RNAi has been shown to have an antiviral effect, but current knowledge of other immunity pathways is limited and none have been implicated in the antiviral response. In the present study, SFV and LGTV replication in selected Ixodes spp. tick cell lines was characterised and the Ixodes scapularis-derived cell line IDE8 was identified as a suitable cell line for this project. Potential antiviral innate immunity pathways were investigated; putative components of the tick JAK/STAT, Toll and Imd pathways were identified by BLAST search using available sequences from well-studied arthropods including the fruit fly Drosophila melanogaster. Using gene silencing, an attempt was made to determine whether these pathways play a role in controlling SFV and LGTV infection in tick cell lines. Selected genes were silenced in IDE8 cells using long target-specific dsRNA and cells were subsequently infected with either SFV or LGTV. Effects of gene silencing on virus replication were assessed by quantitative real time PCR (qPCR) or luciferase reporter assay. Effects on infectious virus production were measured by plaque assay. Replication of the orbivirus St Croix River virus (SCRV), which chronically infects IDE8 cells, was also quantified by qPCR after silencing of selected genes. Interestingly, SFV or LGTV infection of IDE8 cells resulted in a significant increase in SCRV replication, possibly as a result of interference with antiviral pathways by SFV and LGTV or possibly due to diversion of cellular responses from sole control of SCRV. No evidence for an antiviral role for the JAK/STAT or Toll pathways was found in IDE8 cells. However, an antiviral effect was observed for protein orthologues putatively involved in the RNAi response. Argonaute proteins play an important role in translation inhibition and target degradation mediated by RNAi, and silencing of selected Argonaute proteins resulted in a significant increase in SFV and SCRV replication. The carboxypeptidase CG4572 is essential for an efficient antiviral response in D. melanogaster, and supposedly involved in the systemic RNAi response. A putative tick orthologue of CG4572 was identified and this appeared to be involved in the antiviral response in IDE8 tick cells. When expression of CG4572 was silenced and cells subsequently infected with SFV or LGTV, replication of both viruses was significantly increased. In addition, it was shown that three mosquito orthologues of CG4572 also had an antiviral role against SFV in Aedes mosquito cells. In conclusion, of the tick cell lines investigated, IDE8 provided a suitable model system for investigating tick cell responses against arboviruses and new insight into the nature of the tick cell antiviral response was gained.

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Mathematical modeling is a powerful tool to study and analyze the disease dynamics prevalent in the community. This thesis studies the dynamics of two time since infection structured vector borne models with direct transmission. We have included disease induced death rate in the first model to form the second model. The aim of this thesis is to analyze whether these two models have same or different disease dynamics. An explicit expression for the reproduction number denoted by R0 is derived. Dynamical analysis reveals the forward bifurcation in the first model. That is when the threshold value R0 < 1, disease free-equilibrium is stable locally implying that if there is small perturbation of the system, then after some time, the system will return to the disease free equilibrium. When R0 > 1 the unique endemic equilibrium is locally asymptotically stable. For the second model, analysis of the existence and stability of equilibria reveals the existence of backward bifurcation i.e. where the disease free equilibrium coexists with the endemic equilibrium when the reproduction number R02 is less than unity. This aspect shows that in order to control vector borne disease, it is not sufficient to have reproduction number less than unity although necessary. Thus, the infection can persist in the population even if the reproduction number is less than unity. Numerical simulation is presented to see the bifurcation behaviour in the model. By taking the reproduction number as the bifurcation parameter, we find the system undergoes backward bifurcation at R02 = 1. Thus, the model has backward bifurcation and have two positive endemic equilibrium when R02 < 1 and unique positive endemic equilibrium whenever R02 > 1. Stability analysis shows that disease free equilibrium is locally asymptotically stable when R02 < 1 and unstable when R02 > 1. When R02 < 1, lower endemic equilibrium in backward bifurcation is locally unstable. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Vector-borne diseases (VBDs) cause significant harm to humans, plants, and animals worldwide. For instance, VBDs are very difficult to manage, as they are governed by complex interactions. VBD transmission depends on the pathogen itself, vector-host movement, and environmental conditions. Mosquito-borne diseases are a perfect example of how all these factors contribute to changes in VBD dynamics. Although vectors are highly sensitive to climate, modeling studies tend to ignore climate effects. Here, I am interested in the arthropod small vectors that are sensitive to climate factors such as temperature, precipitation, and drought. In particular, I am looking at the effect of temperature on vector traits for two VBDs, namely, dengue, caused by a virus that infects humans and bluetongue disease, caused by a virus that infects ruminants. First, I collect data on mosquito traits' response to temperature changes, this includes adult traits as well as juvenile traits. Next, I use these traits to model mosquito density, and then I incorporate the density into our mathematical models to investigate the effect it has on the basic reproductive ratio R0, a measure of how contagious the disease is. I use R0 to determine disease risk. For dengue, my results show that using mosquito life stage traits response to temperature improves our vector density approximation and disease risk estimates. For bluetongue, I use midge traits response to temperature to show that the suitable temperature for bluetongue risk is between 21.5 °C and 30.7 °C. These results can inform future control and prevention strategies. / Doctor of Philosophy / Infectious diseases are a type of illness that occurs when microorganisms spread between hosts. Some infectious diseases are directly transmitted and some require indirect transmission such as vector-borne diseases (VBDs). Each VBD requires the presence of a vector for the disease to be transmitted. For example, dengue that puts 40% of the world population at risk, requires mosquitoes to transmit the disease between humans. My research aims to investigate how the main climate factor, temperature, influences the spread of VBDs. I develop mathematical and statistical models that explain the effect of temperature on vector traits of a mosquito-borne disease (dengue) and a midge-borne disease (bluetongue) and investigate modeling formulas to improve our estimates for dengue mosquito densities. My results can be used to inform future prevention and control strategies.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Caterina Scoglio / Vector-borne diseases not only cause devastating economic losses, they also significantly impact human health in terms of morbidity and mortality. From an economical and humane point of view, mitigation and control of vector-borne diseases are essential. Studying dynamics of vector-borne disease transmission is a challenging task because vector-borne diseases show complex dynamics impacted by a wide range of ecological factors. Understanding these factors is important for the development of mitigation and control strategies. Mathematical models have been commonly used to translate assumptions concerning biological (medical, demographical, behavioral, immunological) aspects into mathematics, linking biological processes of transmission and dynamics of infection at population level. Mathematical analysis translates results back into biology. Classical deterministic epidemic models do not consider spatial variation, assuming space is homogeneous. Spatial spread of vector-borne diseases observed many times highlights the necessity of incorporating spatial dynamics into mathematical models. Heterogeneous demography, geography, and ecology in various regions may result in different epidemiological characteristics. Network approach is commonly used to study spatial evolution of communicable diseases transmitted among connected populations. In this dissertation, the spread of vector-borne diseases in time and space, is studied to understand factors that contribute to disease evolution. Network-based models have been developed to capture different features of disease transmission in various environments. Network nodes represent geographical locations, and the weights represent the level of contact between regional pairings. Two competent vector populations, Aedes mosquitoes and Culex mosquitoes, and two host populations, cattle and humans were considered. The deterministic model was applied to the 2010 Rift Valley fever outbreak in three provinces of South Africa. Trends and timing of the outbreak in animals and humans were reproduced. The deterministic model with stochastic parameters was applied to hypothetical Rift Valley fever outbreak on a large network in Texas, the United States. The role of starting location and size of initial infection in Rift Valley fever virus spread were studied under various scenarios on a large-scale network. The reproduction number, defined as the number of secondary infections produced by one infected individual in a completely susceptible population, is typically considered an epidemic threshold of determining whether a disease can persist in a population. Extinction thresholds for corresponding Continuous-time Markov chain model is used to predict whether a disease can perish in a stochastic setting. The network level reproduction number for diseases vertically and horizontally transmitted among multiple species on heterogeneous networks was derived to predict whether a disease can invade the whole system in a deterministic setting. The complexity of computing the reproduction number is reduced because the expression of the reproduction number is the spectral radius of a matrix whose size is smaller than the original next generation matrix. The expression of the reproduction number may have a wide range of applications to many vector-borne diseases. Reproduction numbers can vary from below one to above one or from above one to below one by changing movement rates in different scenarios. The observations provide guidelines on executing movement bans in case of an epidemic. To compute the extinction threshold, corresponding Markov chain process is approximated near disease free equilibrium. The extinction threshold for Continuous-time Markov chain model was analytically connected to the reproduction number under some assumptions. Numerical simulation results agree with analytical results without assumptions, proposing a mathematical problem of proving the existence of the relationships in general. The distance of the extinction threshold were shown to be closer to one than the reproduction number. Consistent trends of probability of extinction varying with disease parameters observed through numerical simulations provide novel insights into disease mitigation, control, and elimination.

Chagas disease is caused by the parasite Trypanosoma cruzi and transmitted by multiple triatomine vectors across the Americas. In Central America, the predominant vector is Triatoma dimidiata, a highly adaptable and genetically diverse Hemiptera. In this research, we used a novel reduced-representation DNA sequencing approach to discover community dynamics among multiple biotic factors associated with Chagas disease in Central America, and assess the infestation patterns of T. dimidiata after seasonal and chemical disturbances in Jutiapa, Guatemala. For our first study, we used a hierarchical sampling design to obtain multi-species DNA data found in the abdomens of 32 T. dimidiata specimens from Central America. We aimed to understand (1) the prevalence of T. cruzi infection, (2) the population genetics of the vector and parasite, (3) the blood meal history of the vector, and (4) gut microbial diversity. Our results indicated the presence of nine infected vectors harboring two distinct DTUs: TcI and possibly TcIV. We found significant clusters among T. dimidiata populations in countrywide and within-country levels associated with sylvatic ecotopes and diverse domestic genotypes. There was significantly higher bacteria species richness in infected T. dimidiata abdomens than those that were not infected, with further analysis suggesting that gut bacteria diversity relates to both T. cruzi infection and the local environment. We identified vertebrate blood meals from five T. dimidiata abdomens including chicken, dog, duck and human; however, additional detection methods are necessary to confidently identify blood meal sources. In our second study, we analyzed the GBS genotypes of 440 T. dimidiata specimens collected in two towns of Jutiapa, Guatemala. Our aim was to assess (1) the domestic population patterns that aid the recovery of T. dimidiata after an insecticide treatment in El Carrizal and (2) the seasonal changes that regulate the dispersal of the vector in the untreated communities of El Chaperno. Results showed that the insecticide application was effective at reducing the population abundance immediately after the application in El Carrizal; nevertheless, 18-month post-treatment the town-wide infestation and genetic diversity were recovering. Within-house relatedness among specimens recovered 18 months post-treatment, suggesting that the insecticide treatment failed to fully eliminate domiciliated colonies. In contrast, lack of change in abundance or genetic diversity in El Chaperno implied absence of dispersers from sources beyond the town periphery, while evidence of a decrease of relatedness among individuals implied dispersal among houses. After the insecticide treatment in El Carrizal, population reduction led to lack of genetic spatial autocorrelation; nevertheless, rapid dispersal into neighboring houses lead to autocorrelation 18 months after the insecticide treatment. This pattern was also observed in El Chaperno, where an increase in spatial autocorrelation during seasonal dispersal suggests spillover to close-by households. The creation of a novel genomics pipeline allowed us to understand community and dispersal patterns of T. dimidiata and other biotic factors important for the prevalence and transmission of Chagas disease at local and regional levels. Future studies should include complementary approaches for taxa verification (e.g. bacteria 16S barcoding, PCR-base detection), as well as expand the scope of local population analyses to peridomestic and sylvatic genotypes that could suggest a broader range of vector sources and region-wide patterns of temporal and spatial dispersion.

Climate change will likely have impacts on disease vector distribution. Posing a significant health threat in the 21st century, risk of tick-borne diseases may increase with higher annual mean temperatures and changes in precipitation. We modeled the current and future potential distribution of the Ixodes ricinus tick species in Europe. The Genetic Algorithm for Rule-set Prediction (GARP) was utilized to predict potential distributions of I. ricinus based on current (1990-2010 averages) and future (2040-2060 averages) environmental variables. A ten model best subset was created out of a possible 200 models based on omission and commission criteria. Our results show that under the A2 climate change scenario the potential habitat range for the I. ricinus tick in Europe will expand into higher elevations and latitudes (e.g., Scandinavia, the Baltics, and Belarus), while contracting in other areas (e.g., Alps, Pyrenees, interior Italy, and northwestern Poland). Overall, a potential habitat expansion of 3.8% in all of Europe is possible. Our results may be used to inform climate change adaptation efforts in Europe.

Dengue fever is a debilitating, viral, mosquito-borne disease occurring in tropical and subtropical areas in the world. The majority of dengue cases in the United States were acquired in endemic areas by travelers or immigrants. However, in recent years, autochthonous (locally acquired) dengue cases have been diagnosed in Florida. The purpose of this study was to find an association between potential risk factors and the expansion of dengue fever in the United States. Guided by the eco-bio-social framework, which offers a broad assessment of risk factors for the illness, a retrospective design was used with archival data to correlate changes in climatic variables and imported dengue cases with autochthonous dengue cases in Southeast Florida from 1980 to 2013. A Spearman correlation indicated weak correlations between temperature and autochthonous dengue cases (rs = .999, p = 000) and imported dengue cases with autochthonous dengue cases (rs = .162, p = 000). A negative binomial multivariate regression was used to analyze the expansion of dengue to each monthly unit of temperature, rainfall, and imported dengue cases over 34 years. The results indicated that temperature (IRR = 2.198; 95% CI [1.903, 2.538]) and precipitation (IRR = .991; 95% CI [.988, .994]) were predictors for the geographic expansion of dengue fever in Southeast Florida. The positive social changes include the use of the results to develop an understanding of how climatic variables and migration may influence the expansion of dengue fever to nonendemic regions. The results can be used by public health authorities to address risk factors and to formulate evidence-based decisions in regard to prevention and education concerning dengue fever.

Several vector-borne and zoonotic diseases have emerged or re-emerged in Europe over these last decades. Besides climate change that influences disease risk at a regional scale, landscape changes could be responsible for local heterogeneities in disease risk. Spatial epidemiology tries to understand and predict spatial variations in disease risk by using spatial tools and spatially-explicit modelling methods. This study investigated the impact of fine-grained landscape patterns on the transmission of vector-borne and zoonotic infections in terms of habitat suitability for vectors and/or hosts and of exposure of people to infectious agents. This was studied through three human diseases emerging or at risk of re-emergence in Europe: the rodent-borne Puumala hantavirus, the tick-borne Lyme borreliosis and the mosquito-borne malaria infections. Statistical models were first used to study the relationships between environmental variables and host abundance, host prevalence, and human cases of Puumala hantavirus. Environmental factors were also combined with socio-economic factors to explain Puumala hantavirus and Lyme borreliosis incidence rates. The combination of factors explaining disease transmission and the complexity of such systems led to the development of an innovative, spatially-explicit modelling method: multi-agent simulation (MAS). The MALCAM simulation model was developed to assess the risk of malaria re-emergence in southern France and simulates spatial and temporal variations in contact rate between people and potential malaria vectors. The effect of changes in potential drivers of malaria re-emergence was also simulated. The different case studies showed that fine-grained landscape patterns influence the presence and abundance of vectors and hosts. Moreover, environmental conditions may also influence disease transmission through pathogen dispersal and the exposure of people to infectious agents. Finally, this study showed that people-vector contacts not only depend on the spatial distribution of people and potential vectors, but also on their behaviours and interactions.