PDF(Pubmed)
Abstract：
Schistosomiasis, a prevalent water-borne disease second only to malaria, significantly impacts impoverished rural communities, primarily in Sub-Saharan Africa where over 90% of the severely affected population resides. The disease, majorly caused by Schistosoma mansoni and S. haematobium parasites, relies on freshwater snails, specifically Biomphalaria and Bulinus species, as crucial intermediate host (IH) snails. Targeted snail control is advisable, however, there is still limited knowledge about the community structure of the two genera especially in East Africa. Utilizing a machine learning approach, we employed random forest to identify key features influencing the distribution of both IH snails in this region. Our results reveal geography and climate as primary factors for Biomphalaria, while Bulinus occurrence is additionally influenced by soil clay content and nitrogen concentration. Favorable climate conditions indicate a high prevalence of IHs in East Africa, while the intricate connection with geography might signify either dispersal limitations or environmental filtering. Predicted probabilities demonstrate non-linear patterns, with Bulinus being more likely to occur than Biomphalaria in the region. This study provides foundational framework insights for targeted schistosomiasis prevention and control strategies in the region, assisting health workers and policymakers in their efforts.
摘要：
血吸虫病,一种仅次于疟疾的水传播疾病，对贫困农村社区产生重大影响，主要在撒哈拉以南非洲，那里有超过90％的受严重影响的人口居住。疾病,主要由曼氏血吸虫和血吸虫寄生虫引起，依靠淡水蜗牛，特别是生物phalaria和Bulinus物种，作为关键的中间寄主(IH)蜗牛。有针对性地控制蜗牛是可取的，然而,关于这两个属的群落结构的知识仍然有限，特别是在东非。利用机器学习方法，我们使用随机森林来识别影响该地区两种IH蜗牛分布的关键特征.我们的结果揭示了地理和气候是生物phalaria的主要因素，而鳞茎的发生还受到土壤黏土含量和氮浓度的影响。有利的气候条件表明东非IH的患病率很高，而与地理的复杂联系可能意味着扩散限制或环境过滤。预测概率显示出非线性模式，在该地区，Bulinus比Biomphalaria更有可能发生。这项研究为该地区有针对性的血吸虫病预防和控制策略提供了基础框架见解，协助卫生工作者和决策者的努力。
