Mentor: Dr. Rebecca Poulson Abstract: Wild birds serve as the natural reservoir for influenza A viruses (IAV). Though we know a lot about low pathogenicity (LP) IAV in wild bird hosts in North America, our understanding of the influenza landscape was abruptly altered with the introduction of highly pathogenic (HP) H5 viruses via wild birds late in 2021 into Canada and then across the United States. HP IAV has now been detected in over 100 different avian species in the United States alone and is leading to significant mortality events in some taxa. The long term effects of this introduction, and how it will change the wild bird influenza A system are yet to be fully understood, but we can gain glimpses into what the future may hold for certain species by using serological tools aimed at assessing potential exposure to both LP and HP IAV. The REU scholar will learn both virologic and serologic techniques related to IAV in wild birds and it is anticipated that they will help in screening a subset of serum samples for antibodies to IAV, to better understand the role immunity might play in modulating infection and disease. Is the project computational, empirical, or both? Empirical
Mentor: Dr. Andrew Park group (http://parklab.ecology.uga.edu/) Abstract: Viruses are notoriously capable of jumping between animal species. However, we’re only beginning to understand the roles of seasonality, contact probability, and host demography in this process. In Mongolia, there are as many horses as people, and they routinely get infected with avian influenza viruses as migrating birds stop over at water bodies in proximity to horses. Our group works with an international team to study bird to horse transmission of influenza in this region. This project will use the data we have collected to develop computational models describing transmission between host species. In particular, the goal is to better understand how season, horse proximity to birds, and horse age influence the probability of horses becoming infected. Is the project computational, empirical, or both? Computational, including analysis of data and model building (Using R Studio & R Markdown)
Mentor: Dr. John Drake Abstract: Superspreading in disease dynamics arises when a small number of individuals contribute disproportionately to transmission. Macroparasites, including both internal parasites (e.g., worms) and ectoparasites (e.g. fleas), are widely understood to exhibit the skewed infection burdens that give rise to superspreading. Although causal drivers of different burden distributions within the same host species have often been examined, our understanding of the broader biological and macroecological conditions responsible for shaping such distributions is minimal. Following the model of a previous student who cataloged the wide range of “transmission trees” (another way of quantifying superspreading, see their work here: https://outbreaktrees.ecology.uga.edu/), this student will seek to develop the first meta-database of macroparasite burdens. This database will enable us to ask, for the first time, whether there are ecological or biological traits of hosts and parasites that govern the shape of these distributions and therefore the propensity for superspreading. The ideal student for this project is one who likes to play around with data — to collect it, categorize it, think about it, and analyze it. If you like looking for patterns in the world, this project is you. Is the project computational, empirical, or both? Computational (creating a database from existing published literature; requires working at a computer, but less coding expertise than some other computational projects).
Mentor: Dr. Andy Davis Abstract: Horned passalus beetles are a common forest insect in the eastern United States, and are host to a variety of naturally-occurring parasites, including a nematode that lives in the abdomen (Chondronema passali). Beetles can be heavily parasitized, sometimes with thousands of these worms, though there are many questions about how these nematodes transmit to other beetles. Ongoing work in the Davis lab has sought to determine the impact of these parasites to the host physiology and behavior. Recent projects have revealed how female beetles can be affected more so than males, including influencing their willingness to explore. This implies that the nematode could be causing a behavioral change to its host, to promote its own transmission during oviposition activities. This will be the focal question that will be explored in summer 2023. A student will be tasked with conducting one or more lab-based experiments designed to help elucidate this question. This will include collecting beetles from local forests, housing them in the lab, overseeing behavioral experiments, and performing dissections to determine parasite loads. The details of the projects will be fleshed out when the program starts. The ideal student for this project is someone who is interested in insects, animal behavior, parasites, and who is completely fine with looking for icky, squiggly worms in soil samples or within beetle carcasses. Is the project computational, empirical, or both? Empirical.
Mentor(s): Dr. Pej Rohani group Abstract: Influenza viruses, such as H3N3 and H1N1, are a major cause of illness and death worldwide, accounting for over 3 million severe cases and approximately 500,000 deaths each year. These viruses also have a significant economic impact. The use of predictive modelling has been a useful in detecting new strains of the virus and developing the vaccines against the virus. In this project, we will compare the predictive performance of density-based outlier detection algorithms, specifically the Empirical-Cumulative-distribution-based Outlier Detection (ECOD) algorithm, with isolation forest and one-class support vector machines (SVMs) in detecting unusual physicochemical properties in influenza A viruses. By identifying viruses with significantly different physicochemical properties, we can identify those that may be antigenically distinct, which has important implications for vaccine development and public health efforts. We will use both simulated and benchmark data from Smith et al. for the experiments and fit the models using Python libraries such as Sciklearn, Pandas, Numpy, and PyoD. We will then use R packages, including ggplot2, ggdensity, dplyr, and tidyverse, for data visualization and analysis. The ECOD algorithm has three desirable properties for this task: it can be fitted with limited hyperparameter tuning, it is computationally effective, and it improves interpretability. We will compare the predictive and inferential performance of all three algorithms. The results for isolation forest and one-class SVMs will be provided from previous experiments. We will end the project with a final report. Is the project computational, empirical, or both? Computational.
Mentors: Dr. Kyle Dahlin and Dr. John Vinson Abstract: Climate change is expected to lead to shifts in the risk of mosquito-borne disease outbreaks throughout the world. However, it is unclear how increases in the variability of temperature and rainfall might impact these outbreaks. The goal of this project is to investigate how environmental and demographic noise affect key measures of the transmission of mosquito-borne diseases. The student will be responsible for conducting simulation experiments in R to explore the effect of noise on the probability of an outbreak and the outbreak size distribution. The student will gain experience in the ecology of vector-borne diseases, mathematical modeling, and programming in R. Is the project computational, empirical, or both? Computational.
Mentor(s): Dr. Andreas Handel group (http://handelgroup.uga.edu/) Abstract: Using computer models to study infectious diseases can be challenging for students and researchers who are not trained as modelers. To make this process easier, we have developed several software packages, implemented in the popular R language, to help individuals learn about and analyze infectious disease models both at the individual and population level. The goal of this project is to further advance this software by implementing new features, making tutorials, testing existing features, adding new features, and more. This will increase the usefulness and power of the software and will give future students and researchers better tools to learn about and analyze different infectious diseases. Is the project computational, empirical, or both? This project is computational. We will make use of the R language for all parts of this project.
Mentor(s): Dr. Andreas Handel group (http://handelgroup.uga.edu/) Abstract: A challenge study is a type of trial where patients are intentionally infected with a pathogen. Norovirus, everyone’s favorite cruise ship disease, has been studied in several challenge trials in the past, since its symptoms are relatively mild, and it is difficult to grow in the lab outside of live animals. While certain norovirus challenge studies are widely cited, there is no consensus among experts of exactly how many norovirus challenge studies have been conducted. We will review the published literature to find all previously published challenge studies, and where possible, extract the data from these studies in order to gain a more comprehensive understanding of how many norovirus challenge trials have been conducted and what information they have provided. Is the project computational, empirical, or both? This project is computational and will use the R programming language where necessary. We do not expect this project to be programming intensive.
Mentor(s): Dr. Andreas Handel group (http://handelgroup.uga.edu/) Abstract: Everyone responds differently to the flu shot – the vaccine does not provide 100% protection like some other diseases, and the vaccine needs to be updated every year because flu is able to rapidly evolve and quickly evade immune defenses. The immunological mechanisms for these differences are still not well understood, despite years of previous scientific work. As part of a multi-center collaboration, we will combine data from several studies of seasonal influenza vaccines, and hunt for clues. By leveraging the power of data visualization, machine learning, and statistical modeling techniques, we can identify which aspects of a person, vaccine, or flu strain might affect an individual’s response to the flu vaccine. Is the project computational, empirical, or both? This project will be entirely computational, and will consist of data exploration and statistical analyses. We will use the R programming language, and this project will involve a substantial amount of programming.
Mentors: Dr. Elizabeth M. Warburton and Marcelo Jorge Abstract: Since it was first detected in free-ranging elk in the USA in 1981, chronic wasting disease (CWD) has spread to multiple cervid species, including moose, mule deer, and white-tailed deer, across 30 states. In some locations where this deadly disease has spread, more than 50% of the local deer population is infected. CDW is a prion disease, or transmissible spongiform encephalopathy (TSEs), caused by infectious proteins similar to those that cause bovine spongiform encephalopathy (“mad cow disease”) and Creutzfeldt-Jakob disease in humans. Like all TSEs, CWD is a progressive, fatal disease that affects the brain, spinal cord, and many other tissues. Unlike some other TSEs, CWD can be either directly transmitted by close contact between hosts or environmentally transmitted. The combination of these prions possibly remaining infectious in soil for years and the approximately year-long asymptomatic period in hosts makes understanding the spread of this disease through the white-tailed deer population especially challenging. We use camera-trap, telemetry, and capture-mark-recapture data from our study sites in NW Arkansas to understand the spatiotemporal dynamics of CWD infection in white-tailed deer. By using computational methods such as Bayesian hierarchical models, individual-based models, and machine learning we seek to characterize and predict disease spread. We then communicate our findings to conservation professionals so they can better manage this deadly disease in free-ranging cervids. Is the project computational, empirical, or both? This project is mainly computational but also uses field-collected data. Students will be involved in analyzing camera-trap, telemetry, and capture-mark-recapture data as well as creating simulations of disease spread.
Mentors: Dr. Robbie Richards and Dr. Alex Strauss Abstract: Food web members can dramatically impact host-parasite dynamics through a wide variety of mechanisms. The “healthy herds hypothesis” posits that predators can substantially decrease parasitism in their prey by directly consuming infected individuals. However, experimental tests of this idea remain rare. Moreover, the few experiments that have been attempted have yielded inconsistent results, with predators sometimes decreasing disease, increasing disease, or having no effect on disease in their prey. Reconciling these divergent outcomes is increasingly important as prey species serve as reservoirs for many diseases of concern for spillover into human populations. In this project you will have the opportunity to conduct a large-scale manipulative experiment to test the healthy herds hypothesis. We will use a replicated mesocosm (i.e., artificial pond) system of mosquitofish predators, fungal parasites and their shared host/prey, the water flea, Daphnia dentifera. Finally, we will measure several key traits (e.g., preference of fish for infected vs. healthy hosts) and ask whether they explain why predators increase, decrease, or have no effect on disease in the experiment. Is the project computational, empirical, or both? Mostly empirical, but with opportunities to learn computational skills such as model fitting and parameterization.
Mentors: Dr. Nicole Gottdenker and Juliana Hoyos Abstract: Anthropogenic environmental change has been associated with the emergence of zoonotic vector borne pathogens. In this project, the REU scholar will investigate how parasite burden with the Chagas disease agent Trypanosoma cruzi varies in kissing bug vectors collected across different gradients of deforestation and reforestation in rural areas of central Panama. The student will learn laboratory methods in molecular detection of trypanosomes in kissing bugs and will also apply statistical methods to investigate how land cover, bug abundance, microclimate, stage, and bug population characteristics relate to parasite burden in the bugs. Is the project computational, empirical, or both? Both.