Abstract
Animal movement plays a critical role in disease transmission between farms. However, in the United States, the lack of available animal shipment data sometimes coupled with lack also of detailed information about farm demographics and characteristics presents great challenges for epidemic modeling and prediction. In this study, we propose a new method based on maximum entropy to generate “synthetic” animal movement networks that resemble the “real” networks, considering available statistics about the premises operation type, operation size, and the distance between premises. We illustrate our method for the pig movement networks in Iowa, where we had more “real” data to validate our approach. We then performed network analyses to gain insights into the generated pig movement networks and applied the generated networks to a network-based epidemic model to identify potential vulnerabilities of the network in terms of disease transmission. The model was parameterized for African Swine Fever (ASF) as the US swine industry is currently quite concerned about this disease. ASF outbreaks starting from random farms were associated with highly variable outcomes, ranging from no spreading to large outbreaks. However, outbreaks originating from high out-degree farms may lead to large epidemic sizes. This underscores the importance for stakeholders and policymakers to continue improving animal movement records and traceability programs in the US and the value of making that data available to epidemiologists and modelers to better understand risk and more cost-effectively prevent and control disease transmission. Our approach could be easily adapted to any other livestock system or disease. Future work will include sensitivity analysis to evaluate the impact of our assumptions in the “synthetic” swine movement network and validation of our approach with real-world swine movement information obtained from several production systems in the US.