Identification of high-risk habitats of Oncomelania hupensis, the intermediate host of schistosoma japonium in the Poyang Lake region, China: A spatial and ecological analysis

BackgroundIdentifying and eliminating snail habitats is the key measure for schistosomiasis control, critical for the nationwide strategy of eliminating schistosomiasis in China. Here, our aim was to construct a new analytical framework to predict high-risk snail habitats based on a large sample field survey for Oncomelania hupensis, providing guidance for schistosomiasis control and prevention.Methodology/Principal findingsTen ecological models were constructed based on the occurrence data of Oncomelania hupensis and a range of variables in the Poyang Lake region of China, including four presence-only models (Maximum Entropy Models, Genetic Algorithm for rule-set Production, Bioclim and Domain) and six presence-absence models (Generalized Linear Models, Multivariate Adaptive Regression Splines, Flexible Discriminant Analysis, as well as machine algorithmic models–Random Forest, Classification Tree Analysis, Generalized Boosted Model), to predict high-risk snail habitats. Based on overall predictive performance, we found Presence-absence models outperformed the presence-only models and the models based on machine learning algorithms of classification trees showed the highest accuracy. The highest risk was located in the watershed of the River Fu in Yugan County, as well as the watershed of the River Gan and the River Xiu in Xingzi County, covering an area of 52.3 km2. The other high-risk areas for both snail habitats and schistosomiasis were mainly concentrated at the confluence of Poyang Lake and its five main tributaries.Conclusions/SignificanceThis study developed a new distribution map of snail habitats in the Poyang Lake region, and demonstrated the critical role of ecological models in risk assessment to directing local field investigation of Oncomelania hupensis. Moreover, this study could also contribute to the development of effective strategies to prevent further spread of schistosomiasis from endemic areas to non-endemic areas.

背景：识别并消灭钉螺孳生地是血吸虫病防控的核心举措，对于我国全面消除血吸虫病的国家战略至关重要。本研究旨在基于鄱阳湖（Poyang Lake）地区钉螺（Oncomelania hupensis）的大规模野外抽样调查数据，构建全新的分析框架以预测高风险钉螺孳生地，为血吸虫病防控工作提供科学指导。

研究方法与主要结果：本研究基于鄱阳湖地区的钉螺发生数据与多组环境变量，构建了10种生态预测模型，其中包括4种仅存在数据模型：最大熵模型（Maximum Entropy Models）、规则集生成遗传算法（Genetic Algorithm for rule-set Production）、生物气候模型（Bioclim）以及Domain模型；以及6种存在-缺失数据模型：广义线性模型（Generalized Linear Models）、多元自适应回归样条（Multivariate Adaptive Regression Splines）、柔性判别分析（Flexible Discriminant Analysis），以及机器学习算法模型——随机森林（Random Forest）、分类树分析（Classification Tree Analysis）、广义提升模型（Generalized Boosted Model），用于预测高风险钉螺孳生地。基于整体预测性能评估，本研究发现存在-缺失数据模型的表现优于仅存在数据模型，其中基于分类树的机器学习算法模型准确率最高。最高风险区域位于余干县抚河流域以及星子县赣江与修河流域，总面积达52.3平方千米。其余兼具钉螺孳生与血吸虫病传播风险的高风险区域主要集中于鄱阳湖与其五大主要支流的交汇处。

结论与意义：本研究绘制了鄱阳湖地区全新的钉螺栖息地分布图，证实了生态模型在风险评估中的关键作用，可用于指导钉螺的野外实地调查工作。此外，本研究还可为制定有效防控策略提供参考，以防止血吸虫病从流行区域进一步扩散至非流行区域。