Mathematical Modeling of Infectious Disease Dynamics

Tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact 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 API for extending these templates to address novel scientific research aims. Full methods for EpiModel are detailed in Jenness et al. (2018, ).


Reference manual

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install.packages("EpiModel")

2.6.2 by Samuel Jenness, 20 days ago


https://www.epimodel.org/, https://epimodel.github.io/EpiModel/


Report a bug at https://github.com/EpiModel/EpiModel/issues/


Browse source code at https://github.com/cran/EpiModel


Authors: Samuel Jenness [cre, aut] , Steven M. Goodreau [aut] , Martina Morris [aut] , Adrien Le Guillou [aut] , Chad Klumb [aut] , Skye Bender-deMoll [ctb]


Documentation:   PDF Manual  


GPL-3 license


Imports future, future.apply, graphics, grDevices, stats, utils, collections, ergm, network, RColorBrewer, ape, lazyeval, ggplot2, tibble, methods, rlang, dplyr, coda, networkLite

Depends on deSolve, networkDynamic, tergm, statnet.common

Suggests bslib, DT, ergm.ego, egor, knitr, ndtv, plotly, rmarkdown, shiny, testthat, progressr, tidyr

Linking to Rcpp, ergm


Suggested by statnet, tergmLite.


See at CRAN