An integrated approach to identify distribution of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, in a mountainous region in China.

The aim of this study is to better understand ecological variability related to the distribution of Oncomelania hupensis, the snail intermediate host of Schistosoma japonicum, and predict the spatial distribution of O. hupensis at the local scale in order to develop a more effective control strategy for schistosomiasis in the hilly and mountainous regions of China. A two-pronged approach was applied in this study consisting of a landscape pattern analysis complemented with Bayesian spatial modelling. The parasitological data were collected by cross-sectional surveys carried out in 11 villages in 2006 and mapped based on global positioning system (GPS) coordinates. Environmental surrogates and landscape metrics were derived from remotely-sensed images and land-cover/land-use classification data. Bayesian non-spatial and spatial models were applied to investigate the variation of snail density in relation to environmental surrogates and landscape metrics at the local scale. A Bayesian spatial model, validated by the deviance information criterion (DIC), was found to be the best-fitting model. The mean shape index (MSI) and Shannon's evenness indexes (SEI) were significantly associated with snail density. These findings suggest that decreasing the heterogeneity of the landscape can reduce snail density. A prediction maps were generated by the Bayesian model together with environmental surrogates and landscape metrics. In conclusion, the risk areas of snail distribution at the local scale can be identified using an integrated approach with landscape pattern analysis supported by remote sensing and GIS technologies, as well as Bayesian modelling.