A central component of my research is the development and support of open-source scientific software tools. Currently, my software development is focused on building and extending the EpiModel research platform, which is an integrated toolkit for infectious disease modeling in the R statistical computing language. As part of my applied research, my team also develops web-based apps (written in the Shiny language in R) for interactive exploration of research results.

Software Packages


Tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic agent-based models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an easy application programming interace (API) for extending these templates to address novel scientific research aims.

Research Website    CRAN    Github    Methods Paper


EpiModelHIV is an extension package to EpiModel for simulating network models for HIV transmission dynamics, based on generalized framework of heterosexual mixing within couples in Sub-Saharan Africa and men who have sex with men (MSM) in the United States. In contrast to EpiModel, EpiModelHIV development has been driven by specific research needs versus a general platform, but ongoing expansion efforts will include different subpopulations for persons at risk of infection.



tergmLite addresses the computational burdens of simulating dynamic contact networks with ERGMs by representing these networks in a more data-efficient manner than the more flexible Statnet implementations. This representation results in a 25-50x increase in the speed of simulating these networks within epidemic modeling projects.



EpiModelHPC supports simulating large-scale stochastic network models on modern high-performance computing systems. Functionality provided to simulate models in parallel using either single-node, multiple-core or multiple-node setups.


Web Apps

HIV Preexposure Prophylaxis

This app accompanies the paper Impact of the Centers for Disease Control's HIV Preexposure Prophylaxis Guidelines for Men Who Have Sex With Men in the United States published in the Journal of Infectious Diseases. It allows users to plug in local estimates of HIV PrEP coverage and adherence and it returns predicted HIV incidence rates and related outcomes.

App    Journal Paper

HIV Racial Disparities

This app accompanies the paper Sources of Racial Disparities in HIV Prevalence in Men Who Have Sex with Men in Atlanta, GA, USA: A Modelling Study published in Lancet HIV. One analysis in that paper examined how assumptions about within-group mixing may, by itself, drive or sustain differences in HIV prevalence and incidence. This app, developed in collaboration with the study's lead author Steve Goodreau, allows for interactive exploration of mixing and sexual activity parameters as causes of disparities.

App    Journal Paper

STI Screening Rates

This app accompanies the paper Bacterial STI Screening Rates by Symptomatic Status among Men Who Have Sex with Men in the United States: A Hierarchical Bayesian Analysis published in Sexually Transmitted Diseases. The goal of this app is to allow users to plug in various combinations of demographics, risk, and geography to get out estimates of symptomatic testing and asymptomatic screening rates for bacterial STIs.

App    Journal Paper