ArticleBMC infectious diseases2016
Mapping risk of leptospirosis in China using environmental and socioeconomic data.
Article in BMC infectious diseases, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review.JMIR medical informatics · 2025Pooled it
- Geoprocessing and spatial analysis for identifying leptospirosis risk areas: a systematic review.Revista do Instituto de Medicina Tropical de Sao Paulo · 2020Pooled it
- Ecological Niche Modeling of PathogenicPathogens (Basel, Switzerland) · 2026Article
- Probable Leptospirosis in the Adventure Traveler with Freshwater Exposure: Narrative Review and Case Series.Pathogens (Basel, Switzerland) · 2026Review
- Leptospirosis in Southeast Asia: Investigating Seroprevalence, Transmission Patterns, and Diagnostic Challenges.Tropical medicine and infectious disease · 2026Review
- Unraveling the drivers of leptospirosis risk in Thailand using machine learning.PLoS neglected tropical diseases · 2025Article
- Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach.PLoS neglected tropical diseases · 2025Article
- Spatio-temporal modeling of human leptospirosis prevalence using the maximum entropy model.BMC public health · 2023Article
- Leptospirosis and the Environment: A Review and Future Directions.Pathogens (Basel, Switzerland) · 2023Review
- Spatiotemporal and Seasonal Trends of Class A and B Notifiable Infectious Diseases in China: Retrospective Analysis.JMIR public health and surveillance · 2023Article
- Seroepidemiology of Leptospira serovar Hardjo and associated risk factors in smallholder dairy cattle in Tanzania.PLoS neglected tropical diseases · 2023Article
- Machine learning to predict risk for community-onset Staphylococcus aureus infections in children living in southeastern United States.PloS one · 2023Article
- The ecological and etiological investigation of ticks and rodents in China: results from an ongoing surveillance study in Zhejiang Province.Frontiers in veterinary science · 2023Article
- PathogenicMicroorganisms · 2022Article
- Climate Change Impacts on Microbiota in Beach Sand and Water: Looking Ahead.International journal of environmental research and public health · 2022Review
- Investigating Global Spatial Patterns of Diarrhea-Related Mortality in Children Under Five.Frontiers in public health · 2022Article
- Wild animal and zoonotic disease risk management and regulation in China: Examining gaps and One Health opportunities in scope, mandates, and monitoring systems.One health (Amsterdam, Netherlands) · 2021Review
- Spatio-temporal distribution and agroecological factors associated with canine leptospirosis in Great Britain.Preventive veterinary medicine · 2021Article
- Environmental Risk of Leptospirosis in Animals: The Case of The Republic of Sakha (Yakutia), Russian Federation.Pathogens (Basel, Switzerland) · 2020Article
- Hydroclimatic drivers of highly seasonal leptospirosis incidence suggest prominent soil reservoir of pathogenic Leptospira spp. in rural western China.PLoS neglected tropical diseases · 2019Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLeptospirosis is a water-borne and widespread spirochetal zoonosis caused by pathogenic bacteria called leptospires. Human leptospirosis is an important zoonotic infectious disease with frequent outbreaks in recent years in China. Leptospirosis's emergence has been linked to many environmental and ecological drivers of disease transmission. In this paper, we identified the environmental and socioeconomic factors associated with leptospirosis in China, and predict potential risk area of leptospirosis using predictive models.
methodsLeptospirosis incidence data were derived from the database of China's web-based infectious disease reporting system, a national surveillance network maintained by Chinese Center for Disease Control and Prevention. We built statistical relationship between occurrence of leptospirosis and nine environmental and socioeconomic risk factors using logistic regression model and Maxent model.
resultsBoth logistic regression model and Maxent model have high performance in predicting the occurrence of leptospirosis, with AUC value of 0.95 and 0.96, respectively. Annual mean temperature (Bio1) and annual total precipitation (Bio12) are two most important variables governing the geographic distribution of leptospirosis in China. The geographic distributions of areas at risk of leptospirosis predicted from both models show high agreement. The risk areas are located mainly in seven provinces of China: Sichuan Province, Chongqing Municipality, Hunan Province, Jiangxi Province, Guangdong Province, Guangxi Province, and Hainan Province, where surveillance and control programs are urgently needed. Logistic regression model and Maxent model predicted that 403 and 464 counties are at very high risk of leptospirosis, respectively.
conclusionsOur results highlight the importance of socioeconomic and environmental variables and predictive models in identifying risk areas for leptospirosis in China. The values of Geographic Information System and predictive models were demonstrated for investigating the geographic distribution, estimating socioeconomic and environmental risk factors, and enhancing our understanding of leptospirosis in China.
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