  We introduce Pathogen.jl for simulation and inference of transmission network
individual level models (TN-ILMs) of infectious disease spread in continuous
time. TN-ILMs can be used to jointly infer transmission networks, event times,
and model parameters within a Bayesian framework via Markov chain Monte Carlo
(MCMC). We detail our specific strategies for conducting MCMC for TN-ILMs, and
our implementation of these strategies in the Julia package, Pathogen.jl, which
leverages key features of the Julia language. We provide an example using
Pathogen.jl to simulate an epidemic following a susceptible-infectious-removed
(SIR) TN-ILM, and then perform inference using observations that were generated
from that epidemic. We also demonstrate the functionality of Pathogen.jl with
an application of TN-ILMs to data from a measles outbreak that occurred in
Hagelloch, Germany in 1861(Pfeilsticker 1863; Oesterle 1992).
