Generation of swine movement network and analysis of efficient mitigation strategies for African swine fever virus

Tanvir Ferdousi; Sifat Afroj Moon; Adrian Self; Caterina Scoglio

doi:10.1371/journal.pone.0225785

Generation of swine movement network and analysis of efficient mitigation strategies for African swine fever virus

Tanvir Ferdousi, Sifat Afroj Moon, Adrian Self, Caterina Scoglio

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

Abstract

Animal movement networks are essential in understanding and containing the spread of infectious diseases in farming industries. Due to its confidential nature, movement data for the US swine farming population is not readily available. Hence, we propose a method to generate such networks from limited data available in the public domain. As a potentially devastating candidate, we simulate the spread of African swine fever virus (ASFV) in our generated network and analyze how the network structure affects the disease spread. We find that high in-degree farm operations (i.e., markets) play critical roles in the disease spread. We also find that high in-degree based targeted isolation and hypothetical vaccinations are more effective for disease control compared to other centrality-based mitigation strategies. The generated networks can be made more robust by validation with more data whenever more movement data will be available.

Original language	English
Article number	e0225785
Journal	PLoS ONE
Volume	14
Issue number	12
DOIs	https://doi.org/10.1371/journal.pone.0225785
State	Published - Dec 1 2019
Externally published	Yes

Funding

TF & SAM: Supported by the NSF\NIH \USDA\BBSRC Ecology and Evolution of Infectious Diseases (EEID) Program through USDA-NIFA Award 2015-67013-23818 and by the State of Kansas, National Bio and Agro-Defense Facility (NBAF) Transition Fund through the National Agricultural Biosecurity Center (NABC) at Kansas State University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We would like to thank Dr. Michael W. Sanderson for his useful insights into the swine farming industry. We would also like to thank the developers of the GEMFsim [33] tool which was used to run outbreak simulations.

Funders	Funder number
National Agricultural Biosecurity Center
National Bio and Agro-Defense Facility
state of Kansas
USDA-NIFA	2015-67013-23818
National Science Foundation
National Institutes of Health
U.S. Department of Agriculture
Kansas State University
Biotechnology and Biological Sciences Research Council

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abstract = "Animal movement networks are essential in understanding and containing the spread of infectious diseases in farming industries. Due to its confidential nature, movement data for the US swine farming population is not readily available. Hence, we propose a method to generate such networks from limited data available in the public domain. As a potentially devastating candidate, we simulate the spread of African swine fever virus (ASFV) in our generated network and analyze how the network structure affects the disease spread. We find that high in-degree farm operations (i.e., markets) play critical roles in the disease spread. We also find that high in-degree based targeted isolation and hypothetical vaccinations are more effective for disease control compared to other centrality-based mitigation strategies. The generated networks can be made more robust by validation with more data whenever more movement data will be available.",

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Generation of swine movement network and analysis of efficient mitigation strategies for African swine fever virus

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