title: Assessment of the national schistosomiasis control program in a typical region along the Yangtze River, China.
abstract: Schistosomiasis remains a major public health problem in eastern China, particularly along the Yangtze River Basin. The latest national schistosomiasis control program (NSCP) was implemented in 2005 with the main goal of reducing the rate of infection to less than 5% by 2008 and 1% by 2015. To assess the progress, we applied a Bayesian spatio-temporal model to describe dynamics of schistosomiasis in Guichi, Anhui Province, China, using annual parasitological and environmental data collected within 41 sample villages for the period 2005-2011. Predictive maps of schistosomiasis showed that the disease prevalence remains constant and low. Results of uncertainty analysis, in the form of probability contour maps (PCMs), indicated that the first goal of "infection rate less than 5% by 2008" was fully achieved in the study area. More longitudinal data for schistosomiasis are needed for the assessment of the second goal of "infection rate less than 1% by 2015". Compared with the traditional way of mapping uncertainty (e.g., variance or mean-square error), our PCMs provide more realistic information for schistosomiasis control.
authors.affiliation: Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China; Collaborative Innovation Center of Social Risks Governance in Health, School of Public Health, Fudan University, Shanghai, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China; Collaborative Innovation Center of Social Risks Governance in Health, School of Public Health, Fudan University, Shanghai, China.
authors.forename: Si
authors.initials: S
authors.lastname: Li
authors.affiliation: Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China; Collaborative Innovation Center of Social Risks Governance in Health, School of Public Health, Fudan University, Shanghai, China.
authors.forename: Congcong
authors.initials: C
authors.lastname: Xia
authors.affiliation: School of Epidemiology, Pubic Health and Preventive Medicine, Faculty of Medicine, University of Ottawa, 451 Smyth Rd, Ottawa, Ontario, Canada.
authors.forename: Yue
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China; Collaborative Innovation Center of Social Risks Governance in Health, School of Public Health, Fudan University, Shanghai, China.
authors.forename: Henry
authors.initials: H
authors.lastname: Lynn
authors.affiliation: Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Collaborative Innovation Center of Social Risks Governance in Health, School of Public Health, Fudan University, Shanghai, China.
authors.forename: Tiejun
authors.initials: T
authors.lastname: Zhang
authors.affiliation: Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Collaborative Innovation Center of Social Risks Governance in Health, School of Public Health, Fudan University, Shanghai, China.
authors.forename: Chenglong
authors.initials: C
authors.lastname: Xiong
authors.affiliation: Schistosomiasis Station of Prevention and Control in Guichi Distirct, Anhui Province, China.
authors.forename: Gengxin
authors.initials: G
authors.lastname: Chen
authors.affiliation: Schistosomiasis Station of Prevention and Control in Guichi Distirct, Anhui Province, China.
authors.forename: Zonggui
authors.initials: Z
authors.lastname: He
authors.affiliation: Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China; Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China; Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China; Collaborative Innovation Center of Social Risks Governance in Health, School of Public Health, Fudan University, Shanghai, China. Electronic address: epistat@gmail.com.
authors.forename: Zhijie
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002681:China / Q000453:epidemiology; D019353:Endemic Diseases; D006801:Humans; D017053:Infection Control / Q000379:methods; D011157:Population Dynamics; D015995:Prevalence; D045483:Rivers / Q000469:parasitology*; D012547:Schistosoma / Q000302:isolation & purification*; D012552:Schistosomiasis / Q000097:blood / Q000453:epidemiology* / Q000469:parasitology / Q000517:prevention & control*
keywords: Bayesian spatio-temporal model; China; Probability contour map; Schistosomiasis; Yangtze River Basin; Zero-inflated binomial distribution