@article{JSSv104i04, title={Pathogen.jl: Infectious Disease Transmission Network Modeling with Julia}, volume={104}, url={https://www.jstatsoft.org/index.php/jss/article/view/v104i04}, doi={10.18637/jss.v104.i04}, abstract={<p>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).</p>}, number={4}, journal={Journal of Statistical Software}, author={Angevaare, Justin and Feng, Zeny and Deardon, Rob}, year={2022}, pages={1–30} }