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