Individual-Level Modelling of Infectious Disease Data: EpiILM

In this article we introduce the R package EpiILM, which provides tools for simulation from, and inference for, discrete-time individual-level models of infectious disease transmission proposed by Deardon et al. (2010). The inference is set in a Bayesian framework and is carried out via Metropolis Hastings Markov chain Monte Carlo (MCMC). For its fast implementation, key functions are coded in Fortran. Both spatial and contact network models are implemented in the package and can be set in either susceptible-infected (SI) or susceptible-infected-removed (SIR) compartmental frameworks. Use of the package is demonstrated through examples involving both simulated and real data.

Vineetha Warriyar K. V. , Waleed Almutiry , Rob Deardon

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at

CRAN packages used

R0, EpiEstim, EpiModel, epinet, surveillance, EpiILM, igraph, ergm, adaptMCMC, coda

CRAN Task Views implied by cited packages

gR, Bayesian, Environmetrics, Graphics, Optimization, SocialSciences, Spatial, SpatioTemporal, TimeSeries


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For attribution, please cite this work as

V., et al., "The R Journal:  Individual-Level Modelling of Infectious Disease Data: EpiILM", The R Journal, 2020

BibTeX citation

  author = {V., Vineetha Warriyar K. and Almutiry, Waleed and Deardon, Rob},
  title = {The R Journal:  Individual-Level Modelling of Infectious Disease Data: EpiILM},
  journal = {The R Journal},
  year = {2020},
  note = {},
  doi = {10.32614/RJ-2020-020},
  volume = {12},
  issue = {1},
  issn = {2073-4859},
  pages = {87-104}