  This paper describes the R package EpiILMCT, which allows users to study the
spread of infectious disease using continuous time individual level models
(ILMs). The package provides tools for simulation from continuous time ILMs
that are based on either spatial demographic, contact network, or a combination
of both of them, and for the graphical summarization of epidemics. Model
fitting is carried out within a Bayesian Markov Chain Monte Carlo (MCMC)
framework. The continuous time ILMs can be implemented within either
susceptible-infected-removed (SIR) or susceptible-infected-notified-removed
(SINR) compartmental frameworks. As infectious disease data is often partially
observed, data uncertainties in the form of missing infection times - and in
some situations missing removal times - are accounted for using data
augmentation techniques. The package is illustrated using both simulated and an
experimental data set on the spread of the tomato spotted wilt virus (TSWV)
disease.
