Hill, Alison L., Herrera-Diestra, José L., Tildesley, Michael, Shea, Katriona and Ferrari, Matthew J. (2022) Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey. PLoS Computational Biology, 18 (8). e1010354. doi:10.1371/journal.pcbi.1010354 ISSN 1553-7358.

Preview

PDF WRAP-Cattle-transport-network-predicts-endemic-and-epidemic-foot-and-mouth-disease-risk-on-farms-in-Turkey-Tildesley-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2396Kb) | Preview

Request Changes to record.

Abstract

The structure of contact networks affects the likelihood of disease spread at the population scale and the risk of infection at any given node. Though this has been well characterized for both theoretical and empirical networks for the spread of epidemics on completely susceptible networks, the long-term impact of network structure on risk of infection with an endemic pathogen, where nodes can be infected more than once, has been less well characterized. Here, we analyze detailed records of the transportation of cattle among farms in Turkey to characterize the global and local attributes of the directed—weighted shipments network between 2007-2012. We then study the correlations between network properties and the likelihood of infection with, or exposure to, foot-and-mouth disease (FMD) over the same time period using recorded outbreaks. The shipments network shows a complex combination of features (local and global) that have not been previously reported in other networks of shipments; i.e. small-worldness, scale-freeness, modular structure, among others. We find that nodes that were either infected or at high risk of infection with FMD (within one link from an infected farm) had disproportionately higher degree, were more central (eigenvector centrality and coreness), and were more likely to be net recipients of shipments compared to those that were always more than 2 links away from an infected farm. High in-degree (i.e. many shipments received) was the best univariate predictor of infection. Low in-coreness (i.e. peripheral nodes) was the best univariate predictor of nodes always more than 2 links away from an infected farm. These results are robust across the three different serotypes of FMD observed in Turkey and during periods of low-endemic prevalence and high-prevalence outbreaks.

Item Type:	Journal Article
Subjects:	S Agriculture > SF Animal culture
Divisions:	Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Mathematics
Library of Congress Subject Headings (LCSH):	Cattle -- Transportation -- Turkey, Cattle -- Transportation -- Turkey -- Mathematical models, Foot-and-mouth disease -- Turkey, Foot-and-mouth disease -- Epidemiology -- Mathematical models
Journal or Publication Title:	PLoS Computational Biology
Publisher:	Public Library of Science
ISSN:	1553-7358
Official Date:	19 August 2022
Dates:	Date Event 19 August 2022 Published 3 July 2022 Accepted 9 August 2021 Submitted
Volume:	18
Number:	8
Article Number:	e1010354
DOI:	10.1371/journal.pcbi.1010354
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	6 September 2022
Date of first compliant Open Access:	6 September 2022
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID DEB 1911962 [NSF] National Science Foundation (US) http://dx.doi.org/10.13039/100000001 BB/T004312/1 [BBSRC] Biotechnology and Biological Sciences Research Council http://dx.doi.org/10.13039/501100000268 DEB 1911962 National Institutes of Health http://dx.doi.org/10.13039/100000002 DEB 1911962 National Institute of Food and Agriculture http://dx.doi.org/10.13039/100005825

Request changes or add full text files to a record

Repository staff actions (login required)

View Item

Downloads