Bats have been studied as reservoir hosts of many viruses of concern for human health (e.g., Nipah virus, Ebola virus), but their role in spreading bacterial pathogens remains poorly understood. A key unknown is whether bacteria such as Bartonella spp. and hemotropic mycoplasmas are spread through direct contacts or by arthropod vectors, which is needed to understand how these potentially zoonotic bacteria persist within bat populations and for assessing spillover risks. This computational project is based in the labs of Dr. Richard Hall and Dr. Sonia Altizer and in collaboration with CEID member Dr. Daniel Becker. The REU student will help develop and simulate a mathematical model of bacterial pathogens in vampire bats to understand the relative role of direct contact and arthropod vectors to transmission. The REU student will also have the opportunity to use empirical data from vampire bats sampled over three years to parameterize and validate the model. This project is suitable for students with strong computational skills and a fascination with epidemiology and wildlife or human disease.
Humans are highly social beings and in general we socialize with others that are roughly the same age as us. This age-assortativity of our social circle has important consequences for how infectious diseases spread through populations. Past studies have highlighted how similar the patterns of age-assortativity are across Europe, the United States, and South America. This summer project would expand our knowledge about this topic to populations in an urban African setting: Kampala, Uganda. Analyzing social network survey data collected from 2013-2015 in the Rubaga Division of Kampala, we will use previously developed mathematical tools to estimate the amount of contact between ages at home, work, and school. Why should age-assortativity patterns differ from other countries? How could we use this information to make predictions about infectious disease transmission and persistence?
The student selected for this project will work closely with Paige Miller (PhD student) to organize data, write computer code (in the R programming language), and draft a manuscript for publication in a scientific journal. The project will be supervised by Dr. John Drake (Odum School of Ecology, Director of the Center for the Ecology of Infectious Diseases) and Dr. Chris Whalen (College of Public Health, Director of the Global Health Institute). This is a quantitative and data-science oriented project; we will not be collecting our own data. Long hours of learning how to code and manage data in R will be required. However, the student need not have programming skills as a pre-requisite, only a desire to learn them. An interest in mathematical modeling of infectious diseases, ecology, and global epidemiology is encouraged. If the student has interests in social contact networks, Tuberculosis epidemiology, or mathematical modeling other projects are also possible!
Temporary stressors are a part of life in the animal kingdom, whether they be encounters with predators, transient anthropogenic disturbances or severe weather events. All animals must therefore be capable of dealing with such stressors to ensure their survival, which is the ‘fight-or-flight reaction’. A number of recent research studies, across a range of animal taxa, have found that certain parasites can affect how their hosts deal with these stressors.
In the Davis lab, students have recently conducted a variety of experiments using a common beetle species, the horned passalus (pictured), which is host to a nematode called Chondronema passali. Recent work has shown parasitized beetles have reduced physical strength, cannot fight as well as non-parasitized individuals, and importantly, their stress reactions appear to be affected.
In summer 2018, a project is planned where an REU student will conduct a series of benchtop lab experiments that will all attempt to identify how parasites influence the ability of their hosts to deal with an acute stressor. This will involve field-collection of beetles (from the surrounding area), bringing them to the lab and performing behavioral experiments with them over the summer. The experiments will primarily focus on monitoring changes in stress levels of beetles before and after application of non-lethal stressors.
The ideal student for these projects will be someone who is comfortable handling insects and performing icky dissections, and who can work well in the tick- and chigger-infected field (forest habitats).
Pathogens, such as influenza, need to transmit from infected individuals to other uninfected individuals. Symptoms such as coughing and sneezing help increase transmission. However, in many cases, symptoms can also affect a person’s behavior. Severe symptoms may reduce contacts with others or prevent normal activities. This change in behavior could lead to a reduction in transmission. The goal of this project is to analyze data from a study conducted in college students with Influenza-like illness in which they report if and to what degree their activity level changed. We will use this information to gain a better understanding of how symptoms change behavior and the possible impact on transmission. The results of the study will help inform how future interventions are implemented to maximize the reduction of transmission. This project is quantitative and offered by the Handel group: http://handelgroup.uga.edu/
While there is much evidence that exposure to a larger dose of influenza virus leads to a greater chance of getting an infection, it is less well understood if being infected with a larger dose also means a person is more infectious and has worse symptoms. The goal of this project is to analyze a set of data from human influenza infection studies to determine what the impact of infection doses is on outcomes such as virus load and disease severity. This information will be useful in understanding the importance of keeping exposure to pathogens below certain levels. This project is quantitative and offered by the Handel group: http://handelgroup.uga.edu/
The goal of this project is to develop and use a screening program to assess interactions between virulence factors secreted by intracellular bacterial pathogens and the target cellular organelles. The post-acquisition image processing software package, called OrNet, allows for analysis of tiny changes in organelle shape, spatial distribution, and mass over the course of infection. By examining changes in organelle dynamics during infections with bacterial mutants (each deficient in a single virulence determinant), we aim to determine which bacterial factors affect organelle processes and use that information to better understand bacterial virulence mechanisms and host cell control of organelle function.
This is a quantitative project in the labs of Dr. Shannon Quinn and Dr. Fred Quinn.
Including parasites in the study of community and food web ecology enhances scientific understanding of community structure, function, and species interaction dynamics. Species feeding relationships within a community can also influence parasite transmission, but this has been less studied in terrestrial ecosystems. Students will engage in questions aimed at understanding how food webs influence the transmission of a vector-borne parasite, Trypanosoma cruzi in a theoretical framework. Students will have an opportunity to learn about the ecology of Chagas disease within central Panama, but more specifically about indirect and direct ecological relationships between the primary disease vector Rhodnius pallescens and the surrounding species community within the immediate ecosystem, the Attalea palm crown. They will help in developing food web models for communities across a landscape disturbance gradient and have the opportunity to analyze how feeding relationships between species influence vector abundance and infection status with T. cruzi.
This project is quantitative (applying empirical data to quantitative and qualitative models), and hosted in the lab of Dr. Nicole Gottdenker.
Whooping cough is a highly contagious childhood disease, primarily caused by the bacterium Bordetella pertussis. Resulting from the early success of vaccination strategies in achieving herd immunity pertussis was once considered a candidate for eradication. Alarmingly, pertussis incidence has climbed in some countries (e.g. US, UK) that boast high vaccine uptake, leading to a spate of infant deaths. Mysteriously, the resurgence is not ubiquitous, with pertussis incidence at modest levels in many countries. Much contention surrounds the underlying causes, and strong disagreement remains between clinical data and epidemiological studies. We propose a data science approach to integrate high-resolution epidemiological time series and bacterial genome sequences, to identify the drivers of pertussis transmission, evolution and, ultimately, resurgence. For this we will use a combination of statistical and mathematical models. Specifically, we will focus on the USA as a study case, we will use Bayesian coalescent models for demographic inference that integrate generalized linear models for testing drivers of evolution (such as vaccine changes and demographic changes). We will also develop mathematical models of transmission to quantify selection pressure of mutant introduction in vaccinated populations.
This is a quantitative project in the lab of Dr. Pej Rohani.
The correlates of vaccine-induced immunity are a subject of continued interest for both theoretical and practical reasons. The latter include the need to evaluate the consistency of vaccine production; the susceptibilities of individuals and populations after vaccination. Although the immune system is redundant, almost all current vaccines work through antibodies in serum or on mucosa that block infection or bacteremia/viremia and thus provide a correlate of protection. The functional characteristics of antibodies, as well as quantity, are important. Antibodies may be highly correlated with protection or synergistic with other functions. For pertussis, a vaccine preventable childhood disease, there is no known correlate of protection, the exact level of each of antibodies that are protective is controversial, and in any case, there is no absolute threshold. Using a data science approach, we propose to develop algorithms that will allow determining levels of protection in a systematic approach.
This project is composed of two parts: (i) A literature search to collect data on antibody levels from vaccine trial data and or experimental data; (ii) Developing sets of machine learning classification algorithms to optimize threshold searches and identification. This is a quantitative project hosted in the lab of Dr. Pej Rohani.
Globally, failing water infrastructure has been linked to declining water quality and increased exposure to contaminants, and potentially harmful bacteria infections including, but not limited to Escherichia coli. To assess temporal and spatial changes in the chemical and bacterial composition of water associated with failing water infrastructure in tributaries of the Oconee River Watershed, members of the Capps Lab will conduct a field- and lab-based empirical study. Field activities will be conducted in Athens, GA in stream reaches that have been designated as priority research areas by either Watershed UGA (https://www.watershed.uga.