Project

About EpiModel

EpiModel provides tools for simulating and analyzing mathematical models of infectious disease dynamics. Supported epidemic model classes include deterministic compartmental models, stochastic individual contact models, and stochastic network models. Disease types include SI, SIR, and SIS epidemics with and without demography, with utilities available for expansion to construct and simulate epidemic models of arbitrary complexity. The network model class is based on the statistical framework of temporal Exponential-family Random Graph Models (ERGMs) implemented in the Statnet suite of software for R.

For detailed package documentation, see the EpiModel pkgdown site. The methods underlying EpiModel are described in:

Jenness SM, Goodreau SM, Morris M. EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks. Journal of Statistical Software. 2018; 84(8): 1-47. DOI

Funding Support

The primary support for the development of these software tools and statistical methods has been by two National Institutes of Health (NIH) grants: NIH R01 AI138783 and NIH R01 HD68395. Our applied research projects using EpiModel have received funding from the NIH and Centers for Disease Control and Prevention (CDC). Our team also receives institutional support through center-level NIH grants. A full list of our funding support can be found here.

EpiModel in the Scientific Literature

EpiModel and its extension packages have been used in 134 published scientific studies across HIV/STI epidemiology, COVID-19 modeling, veterinary epidemiology, and other fields. A complete list is maintained on our wiki. If you are aware of others, send us an email at to be included.

Selected publications include:

  • Chandra C, Marcus JL, Jones J et al. PrEP reinitiation, uptake, and persistence as targets for reducing HIV incidence and racial and ethnic inequities among men who have sex with men in Boston, USA: a network modeling study. Lancet Regional Health - Americas. 2026; 63: 101620. DOI
  • Wang J, Clipman SJ, Mehta SH et al. Evaluating spatially targeted HIV interventions and harm reduction services among people who inject drugs in a high-burden setting. Nature Communications. 2026; 17(1): 3102. PubMed
  • Le Guillou AV, Marcus JL, Krakower DS, Violette LR, Jenness SM. Potential risks and benefits of low-barrier access and monitoring for HIV preexposure prophylaxis: a modeling study. Journal of Infectious Diseases. 2026. PubMed
  • Nelson KN, Kiti MC, Shiiba M et al. Characterizing social behavior relevant for infectious disease transmission in four low- and middle-income countries, 2021-2023. Nature Communications. 2025; 16(1): 9586. PubMed
  • Anderle RV et al. Improving social determinants of health significantly reduces AIDS incidence: a modelling study of 1.17 million individuals in Brazil. BMJ Global Health. 2025; 10(7). PubMed
  • Kraft TS, Seabright E, Alami S, Jenness SM et al. Metapopulation dynamics of SARS-CoV-2 transmission in a small-scale Amazonian society. PLoS Biology. 2023; 21(8): e3002108. DOI
  • Man I et al. Evidence-based impact projections of single-dose human papillomavirus vaccination in India: a modelling study. Lancet Oncology. 2022; 23(11): 1419-1429. DOI
  • Goodreau SM, Rosenberg ES, Jenness SM et al. Sources of racial disparities in HIV prevalence among men who have sex with men in Atlanta, GA: a modeling study. Lancet HIV. 2017; 4(7): e311-e320. PubMed
  • Jenness SM, Weiss KM, Goodreau SM et al. Incidence of gonorrhea and chlamydia following HIV preexposure prophylaxis among men who have sex with men: a modeling study. Clinical Infectious Diseases. 2017; 65(5): 712-718. DOI
  • Jenness SM, Goodreau SM, Rosenberg E et al. Impact of the Centers for Disease Control’s HIV preexposure prophylaxis guidelines for men who have sex with men in the United States. Journal of Infectious Diseases. 2016; 214(12): 1800-1807. DOI