  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. The use of the package is demonstrated through examples involving
both simulated and real data.
