ArticleZoonoses and public health2026
Spatiotemporal Early Warning of Human Brucellosis in Hubei Province Using Coupled Ecological-Time Series Models.
Article in Zoonoses and public health, 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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Who cites it
1 citing paper in PubMed.
- Closed-loop artificial intelligence agents for animal epidemic prediction and decision support in livestock farming: a review.Frontiers in veterinary science · 2026Review
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8 authors.
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Abstract
introductionThis study explored transmission patterns of brucellosis in Hubei Province, a Class II region in China, through analysis of spatiotemporal distribution and environmental factors to provide evidence for precise prevention.
methodsWe analysed epidemiological data from Hubei Province (2010-2023), including human cases, demographics, livestock numbers and environmental factors. The MaxEnt model was used for spatial risk prediction, while the seasonal autoregressive integrated moving average (SARIMA) model analysed temporal patterns and warning effectiveness.
resultsHuman brucellosis showed a 'rapid rise-effective control-local rebound' pattern, with incidence rates rising from 0.0087/100,000 (2010) to 0.5932/100,000 (2015), declining to 0.1535/100,000 (2019) and rebounding to 0.4508/100,000 (2023). The epidemic displayed seasonal peaks (April-September) and affected primarily males (71.13%) and farmers (76.54%). Spatial distribution showed a 'high-north low-south' pattern, spreading from Suizhou to neighbouring areas. The MaxEnt model identified high-risk areas (probability ≥ 0.713) in livestock-intensive regions, with sheep density (55.1%) and annual mean temperature (9.9%) as key factors. The SARIMA model indicated earliest warnings in high-risk areas (mid-late April), followed by medium-risk (early-mid April) and low-risk areas (late May).
conclusionsBrucellosis transmission in Hubei is influenced by the livestock industry, control measures and climate. The integrated MaxEnt-SARIMA model provides risk classification and differentiated warning schemes, offering guidance for targeted prevention across different risk areas.
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Registered trials
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