Inference of latent event times and transmission networks in individual level infectious disease models.
Epidemics
Individual level infectious disease model
Julia language
Transmission network
Journal
Spatial and spatio-temporal epidemiology
ISSN: 1877-5853
Titre abrégé: Spat Spatiotemporal Epidemiol
Pays: Netherlands
ID NLM: 101516571
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
received:
11
03
2020
revised:
20
01
2021
accepted:
28
01
2021
entrez:
13
5
2021
pubmed:
14
5
2021
medline:
26
4
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33980405
pii: S1877-5845(21)00010-1
doi: 10.1016/j.sste.2021.100410
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
100410Informations de copyright
Copyright © 2021. Published by Elsevier Ltd.