Who is at risk of getting infected in structured contact networks?

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

Age-structured model for Tuberculosis intervention planning

Kennedy Houck, a junior from Ursinus College, worked with Paige Miller in the lab of Dr. John Drake to study age-based interventions for Tuberculosis.

Abstract:  Tuberculosis (TB) represents a widespread public health concern.  The World Health Organization’s “End TB Strategy” has set the goal for global TB eradication by 2050.  Previous studies have suggested that current public health intervention strategies may not achieve this goal in many parts of the world that experience high TB incidence rates.  The goal of this project was to determine whether age-based interventions could enhance current interventions, which are currently implemented.  A standard TB model, which includes five state variables (Susceptible, Latent, Infectious, Noninfectious, and Removed), was modified to include 16 different age classes, and parameterized with previously published information for India and South Africa.  The model was run for 500 years until equilibrium was reached.  Once equilibrium was reached, 18 different interventions, all simulating faster rates of testing and treating, or shorter infectious periods, among active TB cases, were tested by calculating the rate of decrease of TB cases in each population over time.  A “baseline” scenario where the rate of treatment was held constant was compared to interventions where the infectious period was reduced by 10, 50, 70, and 90% independently for either a specific age class or overall (i.e. a “blanket strategy”).  To test the validity of model predictions, we calculated the correlation between the stable age distribution of cases at equilibrium and WHO TB prevalence data.  In general, age-targeted interventions were found to be more effective at reducing TB cases than the “blanket” strategy.  In India, targeting 15-19 year olds was predicting to result in the greatest overall decline in incidence of both latent and active TB at all intervention levels.  In South Africa, targeting 10-14 year olds was predicted to result in the greatest overall decline of latent TB at all intervention levels; however, targeting 10-14 year olds at lower intervention levels and a blanket strategy at higher intervention levels, were more effective at reducing infectious TB burden.  These results suggest that age-based interventions may complement current public health interventions by further reducing TB burden to achieve WHO eradication goals.  Future studies should utilize a more detailed model for TB dynamics to generate a more realistic prediction.

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