Transmission network reconstruction for foot-and-mouth disease outbreaks incorporating farm-level covariates

• Type of Material: Article
• Authors: Simon M. Firestone, University of Melbourne; Yoko Hayama, National Agriculture Research Organization; Siu Lau, Emory University; Takehisa Yamamoto, National Agriculture Research Organization; Tatsuya Nishi, National Agriculture Research Organization; Richard A. Bradhurst, University of Melbourne; Haydar Demirhan, RMIT University; Mark A. Stevenson, University of Melbourne; Toshiyuki Tsutsui, Natl Agr Res Org
• Language: English
• Date: 2020-07-15
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2020 Firestone et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0235660
• Title of Journal or Parent Work: PLOS ONE
• Volume: 15
• Issue: 7
• Start Page: e0235660
• End Page: e0235660
• Grant/Funding Information: This research was supported by an Australian Research Council Discovery Early Career Researcher Award (project number DE160100477) and by the Japanese Ministry of Agriculture, Forestry and Fisheries (Management Technologies for the Risk of Introduction of Livestock Infectious Diseases and Their Wildlife-borne Spread in Japan, FY2018-2022).
• Keywords: Japan; Dynamics; Science & Technology - Other Topics; 2010 Epidemic; Miyazaki; Experimental infection; Science & Technology; Big; Size; Multidisciplinary Sciences; Virus
• Research Categories: Agriculture, Animal Culture and Nutrition; Health Sciences, Epidemiology; Biology, Veterinary Science; Biology, Biostatistics

Abstract

Transmission network modelling to infer ‘who infected whom’ in infectious disease outbreaks is a highly active area of research. Outbreaks of foot-and-mouth disease have been a key focus of transmission network models that integrate genomic and epidemiological data. The aim of this study was to extend Lau’s systematic Bayesian inference framework to incorporate additional parameters representing predominant species and numbers of animals held on a farm. Lau’s Bayesian Markov chain Monte Carlo algorithm was reformulated, verified and pseudo-validated on 100 simulated outbreaks populated with demographic data Japan and Australia. The modified model was then implemented on genomic and epidemiological data from the 2010 outbreak of foot-and-mouth disease in Japan, and outputs compared to those from the SCOTTI model implemented in BEAST2. The modified model achieved improvements in overall accuracy when tested on the simulated outbreaks. When implemented on the actual outbreak data from Japan, infected farms that held predominantly pigs were estimated to have five times the transmissibility of infected cattle farms and be 49% less susceptible. The farm-level incubation period was 1 day shorter than the latent period, the timing of the seeding of the outbreak in Japan was inferred, as were key linkages between clusters and features of farms involved in widespread dissemination of this outbreak. To improve accessibility the modified model has been implemented as the R package ‘BORIS’ for use in future outbreaks.