Spatially explicit modeling of schistosomiasis risk in eastern China based on a synthesis of epidemiological, environmental and intermediate host genetic data.
- Author(s): Schrader, Matthias;
- Hauffe, Torsten;
- Zhang, Zhijie;
- Davis, George M;
- Jopp, Fred;
- Remais, Justin V;
- Wilke, Thomas
- et al.
Published Web Locationhttps://doi.org/10.1371/journal.pntd.0002327
Schistosomiasis japonica is a major parasitic disease threatening millions of people in China. Though overall prevalence was greatly reduced during the second half of the past century, continued persistence in some areas and cases of re-emergence in others remain major concerns. As many regions in China are approaching disease elimination, obtaining quantitative data on Schistosoma japonicum parasites is increasingly difficult. This study examines the distribution of schistosomiasis in eastern China, taking advantage of the fact that the single intermediate host serves as a major transmission bottleneck. Epidemiological, population-genetic and high-resolution ecological data are combined to construct a predictive model capable of estimating the probability that schistosomiasis occurs in a target area ("spatially explicit schistosomiasis risk"). Results show that intermediate host genetic parameters are correlated with the distribution of endemic disease areas, and that five explanatory variables--altitude, minimum temperature, annual precipitation, genetic distance, and haplotype diversity-discriminate between endemic and non-endemic zones. Model predictions are correlated with human infection rates observed at the county level. Visualization of the model indicates that the highest risks of disease occur in the Dongting and Poyang lake regions, as expected, as well as in some floodplain areas of the Yangtze River. High risk areas are interconnected, suggesting the complex hydrological interplay of Dongting and Poyang lakes with the Yangtze River may be important for maintaining schistosomiasis in eastern China. Results demonstrate the value of genetic parameters for risk modeling, and particularly for reducing model prediction error. The findings have important consequences both for understanding the determinants of the current distribution of S. japonicum infections, and for designing future schistosomiasis surveillance and control strategies. The results also highlight how genetic information on taxa that constitute bottlenecks to disease transmission can be of value for risk modeling.