Inference of latent event times and transmission networks in individual level infectious disease models

Justin Angevaare; Zeny Feng; Rob Deardon

doi:10.1016/j.sste.2021.100410

Inference of latent event times and transmission networks in individual level infectious disease models

Spat Spatiotemporal Epidemiol. 2021 Jun:37:100410. doi: 10.1016/j.sste.2021.100410. Epub 2021 Jan 29.

Authors

Justin Angevaare¹, Zeny Feng², Rob Deardon³

Affiliations

¹ University of Guelph, Canada. Electronic address: https://jangevaare.github.io.
² University of Guelph, Canada. Electronic address: https://zfeng.uoguelph.ca.
³ University of Calgary, Canada. Electronic address: https://people.ucalgary.ca/~robert.deardon/.

PMID: 33980405
DOI: 10.1016/j.sste.2021.100410

Abstract

Transmission networks indicate who-infected-whom in epidemics. Reconstruction of transmission networks is invaluable in applying and developing effective control strategies for infectious diseases. We introduce transmission network individual level models (TN-ILMs), a competing-risk, continuous time extension to individual level model framework for infectious diseases of Deardon et al. (2010). Through simulation study using a Julia language software package, Pathogen.jl, we explore the models with respect to their ability to jointly infer latent event times, latent disease transmission networks, and the TN-ILM parameters. We find good parameter, event time, and transmission network inference, with enhanced performance for inference of transmission networks in epidemic simulations that have higher spatial signals in their infectivity kernel. Finally, an application of a TN-ILM to data from a greenhouse experiment on the spread of tomato spotted wilt virus is presented.

Keywords: Epidemics; Individual level infectious disease model; Julia language; Transmission network.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Communicable Diseases* / epidemiology
Computer Simulation
Epidemics*
Humans
Models, Biological