Abstract: Schistosomiasis japonica is a parasitic disease that debilitates human bodies and greatly impedes socioeconomic progress in endemic areas. It was widespread in southern China several decades ago and the disease prevention effort of the Chinese government and researchers achieved remarkable results in reducing infections. However, in recent year, the epidemic situation has worsened due to a series of changes in the natural environment and socioeconomic conditions. As the only intermediate host of Schistosome, Oncomelania hupensis plays an important role in the spread of this disease and its control is critical for the prevention and control of Schistosome. Therefore, identifying the environmental factors that determine the distribution of the snail could help predict the distribution and extent of snail breeding sites, obtain a macroscopic view on snail spreading trend, and take effective measures to eliminate the snails. In this paper, we aim to determine key indictors that could be used in remote sensing monitoring of Oncomelania hupensis breeding extent and density. Hubei Province is one of the serious epidemic areas in China. Oncomelania hupensis here can be classified into three subtypes: the subtype inside embankments, subtype outside embankments, and subtype in hilly areas, according to the geographical environment of snail habitats. We take into account several environmental factors including elevation, nearest distance to river (water), land use, soil and vegetation to analyze their influence on snail distribution. Geographical Detector Model used in this research is based on spatial variation analysis of the geographical strata to assess the health risks in different environment. It contains four geographical detectors: factor detector identifies which factors are responsible for the risk; ecological detector compares the relative importance of risk factors; risk detector discloses where the high risk areas are; and interaction detector reveals whether the risk factors interact or lead to disease independently. The main procedures of our analysis are as follows: first, both snail statistics and environmental data are collected and preprocessed with ArcGIS Desktop software; then the environmental indicators that are strongly related to snail distribution are identified by the factor detector and ecological detector; finally, favorable (suitable) type or range of each indicator as well as the reference factors that indirectly influence the snails can be computed from the risk detector and interaction detector. It is found that for the subtype inside embankments, vegetation coverage of epidemic season (March to October), especially July to September, determines the extent of distribution, while high density areas are characterized by moderate silt content in soil texture, yellowish red soil and submerged paddy soil, high vegetation coverage in the first quarter of the year. The subtype outsider embankments distributed mainly at lake beaches with high vegetation coverage, while high vegetation coverage in the first quarter, reed and amur silver grass vegetation contributes to its abundance. In hilly areas, there is no clear indicator for the extent of distribution of the subtype due to the relatively complex environment, yet woodland and farmland close to river, waterlogged paddy soil as well as submerged paddy soil are strongly related to high dense of the snails. This result is consistent with previous studies. The result and method of this research could provide scientific reference for policy makers and researchers to take efficient measures to control snail prevalence.

Key words: geographical detector, geographical environmental factor, Hubei Province, key indicator, schistosomiasis, snail