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

View ORCID ProfileSimon M. Firestone, Yoko Hayama, Max S. Y. Lau, Takehisa Yamamoto, Tatsuya Nishi, Richard A. Bradhurst, Haydar Demirhan, Mark A. Stevenson, Toshiyuki Tsutsui
doi: https://doi.org/10.1101/835421
Simon M. Firestone
1Melbourne Veterinary School, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
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  • For correspondence: simon.firestone@unimelb.edu.au
Yoko Hayama
2Viral Disease and Epidemiology Research Division, National Institute of Animal Health, National Agriculture Research Organization, Tsukuba, Ibaraki 305-0856, Japan
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Max S. Y. Lau
3Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
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Takehisa Yamamoto
2Viral Disease and Epidemiology Research Division, National Institute of Animal Health, National Agriculture Research Organization, Tsukuba, Ibaraki 305-0856, Japan
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Tatsuya Nishi
4Exotic Disease Research Station, National Institute of Animal Health, National Agriculture and Food Research Organization, Kodaira, Tokyo, 187-0022, Japan
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Richard A. Bradhurst
5Centre of Excellence for Biosecurity Risk Assessment, The University of Melbourne, Parkville, VIC 3010, Australia
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Haydar Demirhan
6Mathematical Sciences Discipline, School of Science, RMIT University, Melbourne, VIC 3000, Australia
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Mark A. Stevenson
1Melbourne Veterinary School, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
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Toshiyuki Tsutsui
2Viral Disease and Epidemiology Research Division, National Institute of Animal Health, National Agriculture Research Organization, Tsukuba, Ibaraki 305-0856, Japan
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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 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.

Footnotes

  • https://doi.org/10.26188/5cf5e3af414a8

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 08, 2019.
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Transmission network reconstruction for foot-and-mouth disease outbreaks incorporating farm-level covariates
Simon M. Firestone, Yoko Hayama, Max S. Y. Lau, Takehisa Yamamoto, Tatsuya Nishi, Richard A. Bradhurst, Haydar Demirhan, Mark A. Stevenson, Toshiyuki Tsutsui
bioRxiv 835421; doi: https://doi.org/10.1101/835421
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Transmission network reconstruction for foot-and-mouth disease outbreaks incorporating farm-level covariates
Simon M. Firestone, Yoko Hayama, Max S. Y. Lau, Takehisa Yamamoto, Tatsuya Nishi, Richard A. Bradhurst, Haydar Demirhan, Mark A. Stevenson, Toshiyuki Tsutsui
bioRxiv 835421; doi: https://doi.org/10.1101/835421

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