Mapping risk of leptospirosis in China using environmental and socioeconomic data. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Zhao, Jian; Liao, Jishan; Huang, Xu; Zhao, Jing; Wang, Yeping; Ren, Jinghuan; Wang, Xiaoye; Ding, Fan
• Is Part Of: BMC infectious diseases. Volume 16:Issue 1(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2016-12
• Subjects: Leptospirosis -- Ecological niche modeling -- Logistic regression -- Maximum Entropy model -- Geographic Information System; Communicable diseases -- Periodicals; Sexually Transmitted Diseases -- Periodicals; 616.905
• DOI: 10.1186/s12879-016-1653-5
• Languages: English
• ISSNs: 1471-2334

Abstract

Abstract Background Leptospirosis 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. Methods Leptospirosis 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. Results Both 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. Conclusions Our 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.