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Modeling nation-wide U.S. swine movement networks at the resolution of the individual premises

Authors :
Stefan Sellman
Lindsay M. Beck-Johnson
Clayton Hallman
Ryan S. Miller
Katharine A. Owers Bonner
Katie Portacci
Colleen T. Webb
Tom Lindström
Source :
Epidemics, Vol 41, Iss , Pp 100636- (2022)
Publication Year :
2022
Publisher :
Elsevier, 2022.

Abstract

The spread of infectious livestock diseases is a major cause for concern in modern agricultural systems. In the dynamics of the transmission of such diseases, movements of livestock between herds play an important role. When constructing mathematical models used for activities such as forecasting epidemic development, evaluating mitigation strategies, or determining important targets for disease surveillance, including between-premises shipments is often a necessity. In the United States (U.S.), livestock shipment data is not routinely collected, and when it is, it is not readily available and mostly concerned with between-state shipments. To bridge this gap in knowledge and provide insight into the complete livestock shipment network structure, we have developed the U.S. Animal Movement Model (USAMM). Previously, USAMM has only existed for cattle shipments, but here we present a version for domestic swine. This new version of USAMM consists of a Bayesian model fit to premises demography, county-level livestock industry variables, and two limited data sets of between-state swine movements. The model scales up the data to simulate nation-wide networks of both within- and between-state shipments at the level of individual premises. Here we describe this shipment model in detail and subsequently explore its usefulness with a rudimentary predictive model of the prevalence of porcine epidemic diarrhea virus (PEDv) across the U.S. Additionally, in order to promote further research on livestock disease and other topics involving the movements of swine in the U.S., we also make 250 synthetic premises-level swine shipment networks with complete coverage of the entire conterminous U.S. freely available to the research community as a useful surrogate for the absent shipment data.

Details

Language :
English
ISSN :
17554365
Volume :
41
Issue :
100636-
Database :
Directory of Open Access Journals
Journal :
Epidemics
Publication Type :
Academic Journal
Accession number :
edsdoj.208968d26e254cc4ab2fb7e6afd72b48
Document Type :
article
Full Text :
https://doi.org/10.1016/j.epidem.2022.100636