Infectious Disease Transmission Network Modelling with Julia
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Julia is a modern programming language that increases accessibility of high performance computing. We leverage Julia's features in the creation of a high performance package for computationally intensive epidemic models. Specifically, we introduce Pathogen.jl for simulation and inference of transmission network individual level models (TN-ILMs), which are an extension of the individual level model framework of Deardon et al. (2010). TN-ILMs can be used to jointly infer transmission networks, event times, and model parameters within a Bayesian framework via MCMC. We detail our specific strategies for conducting MCMC for TN-ILMs, our implementation of these strategies in Pathogen.jl, and finally provide an example using Pathogen.jl to simulate an epidemic following a susceptible-infectious-removed (SIR) TN-ILM, then performing inference using observations that were generated from that epidemic.
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