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

## TL;DR

This paper introduces a method to estimate animal movement networks in the absence of detailed data, using Iowa's swine industry as a case study to model disease spread risks.

## Contribution

A novel maximum entropy-based method to generate synthetic animal movement networks for epidemic modeling when real data is lacking.

## Key findings

- Premises with central network roles are more vulnerable to disease outbreaks.
- Outbreaks starting at high out-degree farms lead to larger epidemics.
- The system is relatively robust against random disease introductions.

## 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 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.