Precision mapping of snail habitat in lake and marshland areas: Integrating environmental and textural indicators using Random Forest modeling

Schistosomiasis japonica continues to pose a significant public health challenge in China, primarily due to the widespread distribution of <i>Oncomelania hupensis</i>, the sole intermediate host of <i>Schistosoma</i>. This study aims to address the constraints of existing remote sensing analyses for identifying snail habitats, which frequently neglect spatial scale and seasonal variations. To this end, we adopt a multi-source data-driven Random Forest approach that integrates bottomland and ground-surface texture data with traditional environmental variables, enhancing the accuracy of snail habitat assessments. We developed four distinct models for the lake and marshland areas of Guichi, China: a baseline model incorporating ground-surface texture, bottomland variables, and environmental variables; Model 1 with only environmental variables; Model 2 adding ground-surface texture and environmental variables; and Model 3 integrating bottomland with environmental variables. The baseline model outperformed the others, achieving a true skill statistic of 0.93, an accuracy of 0.97, a kappa statistic of 0.94, and an area under the curve of 0.99. Our analysis pinpointed critical high-risk snail habitats distributed in a belt-like pattern along major water bodies, near the Yangtze River, QiuPu River, and around Shengjin Lake, Jiuhua River, and Qingtong River. These insights can aid local health authorities in more efficiently allocating limited resources, developing effective snail surveillance and control strategies to combat schistosomiasis. Additionally, this approach can be adapted to localize other endemic hosts with similar ecological characteristics.