From Global to Local: A Multiscale Geographically Weighted Regression Analysis of Bovine Brucellosis Risk Factors

Bovine brucellosis remains a major zoonotic threat despite ongoing control measures. Conventional strategies often target broad administrative units, potentially overlooking local dynamics relevant to elimination in low‐prevalence settings. We conducted a township‐level spatial epidemiological study in southwestern Hubei Province, China, analyzing serological data from 63,222 cattle in 6335 herds collected in April 2024. Spatial clustering was assessed using Moran’s I, Getis‐Ord General G, and Local Moran’s I (LMi), while multiscale geographically weighted regression (MGWR) evaluated associations with six township‐level covariates: terrain flatness (plain‐to‐hill ratio [PHR]), road network density (RND), cattle density (Cden), goat density (Gden), large‐scale rearing ratio (LSR), and incoming cattle flow (ICF). A distinct high‐risk belt was identified in the southeast‐to‐east‐central region, with positive townships forming high–high clusters. PHR was a significant positive predictor, while RND was negative; MGWR highlighted localized positive effects of LSR. These findings demonstrate the importance of fine‐scale, geographically tailored interventions for brucellosis elimination.