There are many ongoing African initiatives focused on local impacts on population health, with the goal of reducing mortality rates and increasing access to essential medical services. In the past several years, a number of these initiatives have published analyses of impacts of their programs. While these initiatives are programmatically different, they use the same metrics for measuring impacts (e.g., under-five mortality; access to treatment for fever, diarrhea, respiratory infections; vaccine coverage). A database of published results has been constructed, and will be used by the student researcher to better understand different models for accomplishing health related development goals and identifying gaps in the evidence base.
Mentor: John Drake
Type of Project: Quantitative/Computer-based
Ebola and other filoviruses are multi-host pathogens that can infect a wide variety of species, and filovirus emergence presents a pressing threat to human health. Presumably areas that contain large numbers of host species that are susceptible to filoviruses or that contain key reservoir species (such as bats) in high abundance are also areas where the risk of transmission from wild animals to humans is high. However, this hypothesis has rarely been tested. Using data on the location of past spillover events of Ebola, Marburg virus, and other filoviruses in Africa, the goal of this project will be to test what aspects of mammalian host biodiversity (e.g., variation in mammalian species richness, phylogenetic diversity, or ecological diversity) have the greatest impact on spillover risk. Mammalian host data will be drawn from a variety of published sources such as PanTHERIA (a species level database of mammalian trait data) and species range data compiled by the International Union for Conservation of Nature. The project will involve compiling large data sets and analyzing them using the R programming language.
Project Mentor: Patrick Stephens
Type of Project: Quantitative/Computer-based
The tick species Ixodes scapularis (Acari: Ixodidae) is the main vector in the United States for Borrelia burgdorferi, the causative agent of Lyme Disease. Previous research has identified behavioral differences between northern and southern populations of I. scapularis with northern nymphs spending more time above leaf litter increasing the likelihood for human contact. This difference in behavior is observed despite the environment, suggesting an unknown genetic driver to these behavioral patterns. This study will expand on a pilot study using ticks from Connecticut, South Carolina, and Minnesota that identified 99,187 SNPs distributed across 14,168 polymorphic loci using triple-enzyme restriction-site-associated DNA sequences (3RAD). This illustrates that I. scapularis populations have large amounts of intra- and inter-population variation. In this upcoming study we plan to assess 27 populations across the range of I. scapularis to elucidate the genotypes driving behavioral differences and Borrelia transmission. We are also interested in microbiome disparities across this range as it could have an impact onBorrelia transmission. The student will be trained and involved across the pipeline of this study: tick ID, DNA extraction, 3RAD and 16S library prep, and bioinformatics analysis.
Project mentors: Julia Frederick and Travis Glenn
Type of project: Combination of Empirical (lab-based) and Quantitative (computer-based)
Human immunodeficiency virus transmits through networks of people linked through a
range of contacts, including sexual contact and intravenous drug use. SARS and Foot
and Mouth Disease are spread through long-distance movements of infected people
and livestock, followed by local transmission. These outbreaks demonstrate the
important role of networks to transmission of pathogens. Networks can be quantified in
many ways, and an individual’s “importance” to the population can be described with
node “centrality” statistics. Identifying which centrality statistics indicate an individual
has high vulnerability to infection could greatly enhance surveillance and prevention.
However, pathogen transmission routes and human social networks are highly variable
in their structure. For example, sexual contact networks for HIV tend to be assorted by
race. This project will investigate our ability to predict the vulnerability of individuals to
infection when networks are structured in space or social groups. These results could
help us understand when it is worthwhile to estimate node centrality for surveillance
and prevention systems.
The student selected for this project will work closely with Paige Miller (PhD student) to
write computer code (R and python) for disease simulations on networks. 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 simulation-based
project; we will not be collecting our own data. Long hours of learning how to code and
manage data in R will be required. An interest in mathematical modeling of infectious
diseases, ecology, and human sociology is encouraged. The student is free to tailor
this project to their own interests by focusing on specific pathogens or populations!
Mentors: Paige Miller, John Drake, Chis Whalen
Zane Billings, a student at Western Carolina University, worked with Dr. Andreas Handel and graduate student Brian McKay in the UGA College of Public Health.
Influenza-like illnesses (ILIs) present with several of the same symptoms, including cough, fatigue, and weakness. However, ILIs can be caused by a range of different pathogens with vastly different treatments. Quantitative PCR is an incredibly specific and sensitive method to detect several ILIs, but until recently, qPCR methods were prohibitively expensive and required special training and equipment. Recent advances in qPCR technology have allowed for machines such as the Roche cobas Liat system to become available to point-of-care physicians. Using data collected from the University of Georgia Student Health Center, qPCR data was examined relative to patient and physician reported symptoms, as well as impacts and recovery from disease to determine if quantitative estimates of relative viral load are important for physicians to make informed decisions. While relative viral load estimates were found to be correlated to days since the onset of illness and patient temperature at diagnosis, no correlations were found between recovery or severity of illness and relative viral load. However, the study sample was very limited and more research should be performed on broader study populations.
Simran Budhwar from the University of Virginia, worked with Rachel Mercaldo, Brian McKay, and Dr. Andreas Handel to study shedding of Norovirus.
Abstract: Norovirus (NoV) is a common cause of acute gastroenteritis. Symptoms include vomiting and diarrhea, which can lead to complications such as dehydration and also serve to spread viral particles through bodily fluids. While some infections are asymptomatic, infected individuals shed the virus regardless of disease severity, primarily through stool. To understand the dynamics of viral shedding, previous studies measured viral load in healthy human subjects challenged with various doses of the virus. In the present analysis, data from these studies was combined to better describe NoV shedding over time. We calculated key variables such as peak viral titer, time to peak viral titer, and duration of shedding, in addition to estimating total shedding through the area under the curve (AUC) value of each participant’s total shedding time-series curve. On average, patients shed the virus for 22 days, with the peak viral titer appearing on day 5 following challenge. Peak viral titers were 10.551 (log10) genomic equivalence copies per gram of stool, while AUC averaged at 11.58 (log10) genomic equivalence copies per gram stool. Though these are key variables that are necessary to understand viral shedding, future work should focus on exploring the drivers of variation in viral load and shedding, such as symptoms or other patient-specific factors.
Michael Lansford, a student at the University of Rochester, worked with Dustin Dial and Dr. Gaelen Burke
are sap-sucking insects that contain bacterial endosymbionts to help them synthesize
essential amino acids. The adelgid life cycle alternates between sexual
generations that parasitize spruce as a primary host and asexual generations
that parasitize a secondary host plant species. The adelgid family consists of
five lineages each with a different secondary host plant: Douglas fir, fir,
hemlock, larch, and pine. Each adelgid lineage has a different pair of symbionts,
a primary symbiont that was acquired by the adelgid first and a secondary
symbiont that was acquired second. Vallotia is
a symbiont shared between the Douglas fir lineage, where it is the secondary symbiont,
and the larch lineage, where it is the primary symbiont. To determine the nutritional
roles of Vallotia in different species,
genomic data were searched for genes involved in amino acid synthesis. FastQC
was used to evaluate the quality of raw adelgid read data. The Georgia Advanced
Computing Research Center (GACRC) cluster was used to assemble and annotate
genomes from the raw reads. After running scripts to assemble raw reads into
scaffolds, BLAST was used to identify which scaffolds were from symbionts.
Symbiont genes were annotated using PROKKA and Geneious Prime and biochemical
pathways were reconstructed with help from BioCyc. The results showed that Vallotia
is primarily responsible for synthesis of all essential amino acids
except cysteine in the Douglas fir lineage species A.
the primary symbiont in the Douglas fir lineage, works together with Vallotia
in lysine and aromatic amino acid synthesis. In both larch lineage
species, Vallotia is only responsible for
the final steps in tryptophan synthesis and depends on the secondary symbiont Profftia
in A. lariciatus and probably A.
abeitis for most steps in aromatic synthesis. These results suggest that Vallotia
was acquired by the Douglas fir lineage to account for the loss of
most synthesis genes in Gillettellia and Profftia
was acquired by the larch lineage to account for the loss of
aromatic synthesis genes in Vallotia.
Taryn Waite, a student at Colby College, collaborated with REU student Courtney Schreiner, Nicole Solano, Dr. Craig Osenberg, and Dr. Courtney Murdock.
Conspecific density in larval habitats is an important factor affecting adult
fitness in Aedes albopictus mosquitoes, as it drives competition for
food and space. We conducted a larval density experiment wherein mason jars
containing leaf infusion and varying numbers of larvae were placed in a field
enclosure, developmental stage was recorded daily, and emerged adults were
collected. Nonlinear regressions were performed on the data for survival to
adulthood, sex ratio of adults, and wing length of females, and fecundity was
inferred from wing length. Using these regressions, an equation was created to
predict short-term population dynamics in habitats with varying conspecific
densities. What determines the densities that will actually occur in various
larval habitats is where females choose to lay their eggs. Female mosquitoes
have the ability to skip-oviposit, which entails spreading their eggs out among
multiple habitats instead of dumping them all in one habitat. The population
dynamics equation was used to evaluate the theoretical consequences of skip-
versus non-skip- oviposition, using scenarios with varying numbers of
egg-laying females and a fixed number of available larval habitats. We found
that at low densities of ovipositing females, skip-oviposition produces more
short-term population growth than non-skip-oviposition. At higher densities,
non-skipping becomes more productive than skipping, though there is less
divergence between the outcomes. This simulation demonstrates a way in which
patterns of density-dependence could act as a link between oviposition behavior
and population dynamics. Due to the effects that we found of density dependence
in larval habitats, individual females’ oviposition behavior could have
consequences for short-term population dynamics.
Juliana Taube, a student at Bowdoin College, worked with Paige Miller and Dr. John Drake.
Abstract: Transmission trees contain valuable
details about who infected whom in infectious disease outbreaks. We created a
database with 81 published, standardized transmission trees consisting of 12 directly-transmitted
pathogens (mostly viruses). We also demonstrated how the database could be used
to help answer research questions in infectious disease epidemiology. First, we
analyzed overall and pathogen-specific patterns between tree parameters (R0 and
variation in secondary infections). We found that outbreak size is nonlinearly
associated with R0 and the dispersion parameter, but emphasize
that pathogen-specific patterns and intervention efforts may alter theoretical
relationships between these variables. Second, we examined how superspreader
contribution to onward transmission, either directly or through their tree
descendants, varies across pathogens. Superspreaders were responsible for most
cases via their descendants and the number of superspreaders varied across
pathogens. Additional database exploration matched theory1 about
how the proportion of superspreaders increases at intermediate levels of
dispersion, an idea that should be further explored. We hope that our database
will assist both theoretical and applied infectious disease epidemiology
research in the future.
1. Lloyd-Smith, JO, Schreiber, SJ, Kopp, PE, & Getz, WM (2005)
“Superspreading and the effect of individual variation on disease emergence.”Nature, 438(7066):
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. This project is quantitative. We will make use of the R language for all parts of this project. The project is offered by the Handel group.
Host Laboratory: Andreas Handel
Type of project: Quantitative/Computer-based