Inference of latent event times and transmission networks in individual level infectious disease models. (June 2021)

• Record Type: Journal Article
• Authors: Angevaare, Justin; Feng, Zeny; Deardon, Rob
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 37(2021)
• Publication Date: 2021-06
• Subjects: Individual level infectious disease model -- Transmission network -- Epidemics -- Julia language; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2021.100410
• Languages: English
• ISSNs: 1877-5845

Abstract

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.