athogen.jl: Infectious Disease Transmission Network Modeling with Julia

Dublin Core

Title

athogen.jl: Infectious Disease Transmission Network Modeling with Julia

Subject

epidemic, individual level models, transmission networks, Bayesian, Julia.

Description

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).

Creator

Justin Angevaare

Source

https://www.jstatsoft.org/article/view/v104i04

Publisher

University of Guelph

Date

September 2022

Contributor

Fajar bagus W

Format

PDF

Language

English

Type

Text

Files

Citation

Justin Angevaare, “athogen.jl: Infectious Disease Transmission Network Modeling with Julia,” Repository Horizon University Indonesia, accessed April 5, 2025, https://repository.horizon.ac.id/items/show/8274.