ArticleTransboundary and emerging diseases2026
From Global to Local: A Multiscale Geographically Weighted Regression Analysis of Bovine Brucellosis Risk Factors.
Article in Transboundary and emerging diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- From Global to Local: A Multiscale Geographically Weighted Regression Analysis of Bovine Brucellosis Risk Factors.Transboundary and emerging diseases · 2026Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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