乳牛農場の感染症脆弱性指数（IDVI）の開発：リスク評価と生物学的安全対策のためのデータ駆動型アプローチ

Dairy farms are vulnerable to a wide range of infectious diseases, which can have significant economic and public health consequences. However, there remains a lack of a robust, farm-level tool that assesses their disease vulnerability. The primary objective of this study was to develop and validate the Infectious Disease Vulnerability Index (IDVI) as a tool for assessing dairy farm vulnerability to disease, aimed at enhancing biosecurity strategies and prioritizing high-risk farms for targeted interventions. Survey data on biosecurity practices, farm management, and animal movement were analyzed using K-Prototypes clustering, dimensionality reduction, optimal cluster determination, stability assessment, and validation. The clustering analysis revealed three distinct farm clusters, characterized by low, medium, and high disease vulnerability index categories. Cluster quality was assessed using silhouette scores (mean = 0.69) and Davies-Bouldin Index (mean = 0.44), with bootstrap resampling (200 iterations) confirming stability, and Friedman's Test showing statistically distinct risk clusters. Following permutation testing, the factors with the most impact on the infectious disease vulnerability clusters included specific farm characteristics such as organic status and the total number of dairy animals, animal movement practices including frequency of bull calf movements and the average number of heifers leaving the farm during a shipment, farm contacts including hunters and dead animal haulers, and farm biosecurity practices including farm equipment transport practices, engaging in dairy animal exhibitions, and not having quarantine facilities with water, feed, and air separated from other farm animals. The IDVI offers a promising measure of comparing disease risk profiles, validated through bootstrapped silhouette scoring, statistical analysis, and permutation importance analysis. By leveraging the IDVI, dairy farmers, veterinarians, and animal health authorities can identify high-risk farms, implement targeted interventions to improve biosecurity, reduce disease transmission, and consequently enhance their outbreak preparedness.