ArticleScientific reports2025
Risk effects of environmental factors on human brucellosis in Aksu Prefecture, Xinjiang, China, 2014-2023.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Spatiotemporal Early Warning of Human Brucellosis in Hubei Province Using Coupled Ecological-Time Series Models.Zoonoses and public health · 2026Article
- Spatiotemporal dynamics and environmental factors of human brucellosis in mainland China, 2004-2022.BMC infectious diseases · 2026Article
- Prevalence and spatial distribution characteristics of human brucellosis in Ningxia from 2010 to 2024.PLoS neglected tropical diseases · 2026Article
- Assessing the spatiotemporal dynamics and driving factors of human brucellosis in Northern Xinjiang, China (2015-2023).Tropical medicine and health · 2026Article
- Climate-Sensitive Transmission of Human Brucellosis: A Systematic Review of Climatic and Environmental Determinants in the Middle East.Journal of tropical medicine · 2026Review
- Ecological study of human brucellosis in Iran from 2000 to 2023 and the limited role of climatic factors.Scientific reports · 2025Article
- Spatiotemporal trends and ecological determinants of human brucellosis among 31 provinces in mainland China, 2004-2021: a Bayesian spatiotemporal modeling study.BMC public health · 2025Article
- Interactions between brucellosis and environmental factors: spatiotemporal epidemiology from Xinjiang, China.BMC public health · 2025Article
- Multiscale environmental drivers of human brucellosis transmission in Xinjiang: A spatiotemporal analysis integrating GAM and MaxEnt modeling (2015-2023).PLoS neglected tropical diseases · 2025Article
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8 authors.
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Abstract
The context of rapid global environmental change underscores the pressing necessity to investigate the environmental factors and high-risk areas that contribute to the occurrence of brucellosis. In this study, a maximum entropy (MaxEnt) model was employed to analyze the factors influencing brucellosis in the Aksu Prefecture from 2014 to 2023. A distributed lag nonlinear model (DLNM) was employed to investigate the lagged effect of meteorological factors on the occurrence of brucellosis. A total of 17 environmental factors were identified as affecting the distribution of brucellosis to varying degrees. The largest contributing was the normalized difference vegetation index (NDVI), followed by gross domestic product (GDP), and then meteorological factors such as average temperature, average relative humidity, and average wind speed. The receiver operating characteristic (ROC) curve demonstrated that the MaxEnt model exhibited a high degree of predictive efficacy, with an area under the curve (AUC) value of 0.921. The impact of high temperature (25℃ with a 2-month lag, RR = 3.130, 95% CI 1.642 ~ 5.965), low relative humidity (28% with a 2.5-month lag, RR = 1.795, 95% CI 1.298 ~ 2.483), and low wind speed (1.9 m/s with a 0-month lag, RR = 2.408, 95% CI 1.360 ~ 4.264) are the most significant meteorological factors associated with the incidence of brucellosis. The trends in the impact of extreme meteorological conditions on the spread of brucellosis were found to be generally consistent. Stratified analyses indicated that males were more affected by meteorological factors than females. The prevalence of brucellosis is influenced by a range of socio-economic and meteorological factors, and a multifaceted approach is necessary to prevent and control brucellosis.
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