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

被引：1

作者：

Angevaare, Justin ^{[1
]}

Feng, Zeny ^{[1
]}

Deardon, Rob ^{[2
]}

机构：

[1] Univ Guelph, Guelph, ON, Canada

[2] Univ Calgary, Calgary, AB, Canada

来源：

SPATIAL AND SPATIO-TEMPORAL EPIDEMIOLOGY | 2021年 / 37卷

基金：

加拿大自然科学与工程研究理事会;

关键词：

Individual level infectious disease model; Transmission network; Epidemics; Julia language;

D O I：

10.1016/j.sste.2021.100410

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

摘要：

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. (C) 2021 Elsevier Ltd. All rights reserved.

引用

页数：10

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