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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. [ABSTRACT FROM AUTHOR]

Background: Guangdong Province is one of the most developed and populous provinces in southern China, with frequent foreign exchanges and large transient population. The annual number of cases of HIV/AIDS reported in Guangdong has been higher than most of provinces in China for several successive years. HIV infection by heterosexual transmission occurs across the province, with transmission among men who have sex with men occurring mainly in larger urban centers. There is a lack of widespread and representative data on the distribution of HIV subtypes in Guangdong. This study aimed to thoroughly investigate and estimate the prevalence and distribution of HIV-1 subtypes using a city-based sampling strategy to better understand the characteristics of HIV transmission in Guangdong.Methods: Archived plasma samples (n = 1205) from individuals diagnosed as HIV-1 infection in 2013 were selected randomly from all 21 cities in Guangdong Province. Genotypes were determined using env and/or gag sequences using phylogenetic analysis. The distributions of HIV genotypes in different risk groups and different cities were analyzed.Results: A total of 15 genotypes, including six discordant genotypes, were identified. The four main HIV-1 subtypes in Guangdong were CRF01_AE (43.2%), CRF07_BC (26.3%), CRF55_01B (8.5%), and CRF08_BC (8.4%). CRF01_AE was the predominant subtype in all risk populations. The high mobility of people shaped the complexity of the HIV genotypes, while the switch of risk factors affected the distribution and future trend of HIV-1 genotypes in Guangdong. Another epicenter located in the western region in addition to the known epicenter cities in the Pearl River Delta region of Guangdong may exist.Conclusions: Our study provides a comprehensive molecular epidemiologic dataset to understand the diversity and distribution of HIV genotypes in Guangdong, as well as to clarify the unique region- and risk group-specific transmission dynamics. The results provide critical and insightful information for more effective intervention strategies to limit HIV transmission in the future. [ABSTRACT FROM AUTHOR]