Animal movement estimation and network-based epidemic modeling: Illustration for the swine industry in Iowa (US)

• Published in: PloS one Vol. 20; no. 6; p. e0326234
• Main Authors: Yang, Qihui, Martínez-López, Beatriz, Moon, Sifat Afroj, Gomez-Vazquez, Jose Pablo, Scoglio, Caterina
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.06.2025; Public Library of Science (PLoS)
• Subjects: African swine fever; African Swine Fever - epidemiology; African Swine Fever - transmission; Agriculture; Animal Husbandry; Animal production; Animals; Biology and Life Sciences; Centrality; Computer and Information Sciences; Disease control; Disease Outbreaks - veterinary; Disease prevention; Disease spread; Disease transmission; Diseases; Entropy; Environmental aspects; Epidemic models; Epidemics; Farms; Health aspects; Hogs; Infectious disease modeling; Infectious diseases; Inventory; Iowa; Iowa - epidemiology; Livestock; Maximum entropy; Medical risk factors; Medicine and Health Sciences; Methods; Modelling; Network analysis; Outbreaks; People and places; Population; Probability; Risk assessment; Social behavior in animals; Swine; Veterinary epidemiology; Iowa; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0326234

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 a lack of detailed information about farm demographics and characteristics, presents great challenges for epidemic modeling and prediction. In this study, we proposed a new method based on the maximum entropy to generate “synthetic” animal movement networks, considering available statistics about the premises operation type, operation size, and the distance between premises. We illustrated our method for the swine movement networks in Iowa and performed network analyses to gain insights into the swine industry. We then applied the generated networks to a network-based epidemic model to identify potential system vulnerabilities in terms of disease transmission. The model was parameterized for African Swine Fever (ASF) as the US swine industry is quite concerned about this disease. Results show that premises with a central role in the network are more vulnerable to disease outbreaks and play an important role in disease spread. Simulations with outbreaks starting from random farms reveal no significant large outbreaks, indicating the system’s relative robustness against arbitrary disease introductions. However, outbreaks originating from high out-degree farms can 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 inform strategies aimed to cost-effectively prevent and control disease transmission. Our approach could be easily adapted to estimate movement networks in other animal production systems and to inform disease spread models for various infectious diseases.