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

TL;DR

This paper presents a method to generate swine movement networks from limited public data, analyzes ASFV spread within these networks, and evaluates targeted mitigation strategies for disease control.

Contribution

It introduces a novel approach to create swine movement networks from limited data and assesses effective mitigation strategies for African swine fever.

Findings

High in-degree farms are critical in disease spread

Targeted isolation of high in-degree farms is effective

Network robustness improves with more data

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.