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 over 125 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:

  • 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
  • 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
  • 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
  • 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
  • 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
  • Jenness SM, Le Guillou A, Chandra C et al. Projected HIV and bacterial STI incidence following COVID-related sexual distancing and clinical service interruption. Journal of Infectious Diseases. 2021; 223(6): 1019-1028. PubMed
  • Jenness SM, Johnson JA, Hoover KW et al. Modeling an integrated HIV prevention and care continuum to achieve the Ending the HIV Epidemic goals. AIDS. 2020; 34(14): 2103-2113. PubMed
  • Jenness SM, Maloney K, Smith SK et al. Addressing gaps in HIV preexposure prophylaxis care to reduce racial disparities in HIV incidence in the United States. American Journal of Epidemiology. 2019; 188(4): 743-752. PubMed