From Global to Local: A Multiscale Geographically Weighted Regression Analysis of Bovine Brucellosis Risk Factors
Zihan Tian1,2, Yingying Dong1,2, Peng Yuan3
1National Key Laboratory of Agricultural Microbiology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, Hubei, China, hzau.edu.cn.
Transboundary and Emerging Diseases
|June 8, 2026
Summary
Bovine brucellosis control needs localized strategies. A spatial study identified a high-risk area in Hubei, China, emphasizing fine-scale interventions for disease elimination.
Area of Science:
- Veterinary Epidemiology
- Zoonotic Disease Control
- Spatial Analysis
Background:
- Bovine brucellosis is a significant zoonotic disease.
- Current control strategies may miss local transmission dynamics.
Purpose of the Study:
- To conduct a township-level spatial epidemiological study of bovine brucellosis in Hubei Province, China.
- To identify high-risk areas and associated environmental and demographic factors for targeted interventions.
Main Methods:
- Spatial clustering analysis (Moran's I, Getis-Ord General G, LMi) of serological data from 63,222 cattle.
- Multiscale geographically weighted regression (MGWR) to assess associations with terrain flatness (PHR), road network density (RND), cattle density (Cden), goat density (Gden), large-scale rearing ratio (LSR), and incoming cattle flow (ICF).
Main Results:
- A distinct high-risk belt for bovine brucellosis was identified in the southeast-to-east-central region.
- Terrain flatness (PHR) was a significant positive predictor, while road network density (RND) was a negative predictor.
- Localized positive effects of the large-scale rearing ratio (LSR) were highlighted by MGWR.
Conclusions:
- Fine-scale spatial analysis is crucial for understanding bovine brucellosis transmission.
- Geographically tailored interventions are essential for effective elimination, especially in low-prevalence settings.
- Identifying localized risk factors like terrain and farming practices can optimize control strategies.
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