A Slaughterhouse-Linked Spatial Framework for Prioritizing Livestock Surveillance in Thailand

(1) Background: Slaughterhouses receive livestock from different sources, enabling the collection of health-related surveillance information. Few surveillance frameworks integrate slaughterhouse distribution with livestock population density for prioritizing surveillance areas. (2) Methods: The surveillance priority of livestock in Thailand was determined on a district basis. Registered slaughterhouses were mapped using kernel density estimation. Livestock were mapped using census data. The z-scores were combined to determine the composite surveillance prioritization index for each district. The 90th percentile was used to determine district-level surveillance priority. Spatial analyses using Global Moran's I and LISA statistics were performed. Sensitivity analyses assessed robustness. (3) Results: The composite district-level priority index ranged from -0.728 to 9.071 (I = 0.642, p < 0.001). LISA identified 120 High-High (HH) priority districts. Of these, 42 priority districts were selected, concentrated in the northeastern and west-central regions. (4) Conclusions: This model is replicable and can be applied to other regions that rear livestock and use slaughterhouses for surveillance.