title: Nonparametric spatial analysis to detect high-risk regions for schistosomiasis in Guichi, China.
abstract: Schistosomiasis control in China is facing a new challenge due to the rebound of epidemics in many areas and the unsustainable effects of the chemotherapy-based control strategy. Identifying high-risk regions for schistosomiasis is an important first step for an effective and sustainable strategy. Direct surveillance of snail habitats to detect high-risk regions is costly and no longer a desirable approach, while indirect monitoring of acute schistosomiasis may be a satisfactory alternative. To identify high-risk regions for schistosomiasis, we jointly used multiplicative and additive models with the kernel smoothing technique as the main approach to estimate the relative risk (RR) and excess risk (ER) surfaces by analyzing surveillance data for acute schistosomiasis. The feasibility of detecting high-risk regions for schistosomiasis through nonparametric spatial analysis was explored and confirmed in this study, and two significant high-risk regions were identified. The results provide useful hints for improving the national surveillance network for acute schistosomiasis and possible approaches to utilizing surveillance data more efficiently. In addition, the commonly used epidemiological indices, RR and ER, are examined and emphasized from the spatial point of view, which will be helpful for exploring many other epidemiological indices.
authors.affiliation: Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, People's Republic of China.
authors.forename: Zhijie
authors.initials: Z
authors.lastname: Zhang
authors.forename: Allan B
authors.initials: AB
authors.lastname: Clark
authors.forename: Roger
authors.initials: R
authors.lastname: Bivand
authors.forename: Yue
authors.initials: Y
authors.lastname: Chen
authors.forename: Tim E
authors.initials: TE
authors.lastname: Carpenter
authors.forename: Wenxiang
authors.initials: W
authors.lastname: Peng
authors.forename: Yibiao
authors.initials: Y
authors.lastname: Zhou
authors.forename: Genming
authors.initials: G
authors.lastname: Zhao
authors.forename: Qingwu
authors.initials: Q
authors.lastname: Jiang
mesh_terms: D000818:Animals; D002681:China / Q000453:epidemiology; D004197:Disease Reservoirs; D006801:Humans; D008954:Models, Biological; D018570:Risk Assessment; D012554:Schistosomiasis japonica / Q000453:epidemiology*; D012908:Snails; D013029:Space-Time Clustering