Schistosomiasis is a vector-borne disease transmitted by freshwater snails and is prevalent in rural areas with poor sanitation and no access to tap water. Three snail species are known to transmit schistosomiasis in South Africa (SA), namely Biomphalaria pfeifferi, Bulinus globosus and Bulinus africanus. In 2003, a predicted prevalence of 70% was reported in tropical climates in SA. Temperature and rainfall variability can alter schistosomiasis-transmitting snails' development by increasing or decreasing their abundance and geographical distribution. This study aimed to map the historical distribution of schistosomiasis from 1950 to 2006 in SA. The snail sampling data were obtained from the historical National Snail Freshwater Collection (NFSC). Bioclimatic variables were extracted using ERA 5 reanalysis data provided by the Copernicus Climate Change Service. In this study, we used 19 bioclimatic and four soil variables. The temporal aggregation was the mean climatological period pre-calculated over the 40-year reference period with a spatial resolution of 0.5° x 0.5°. Multicollinearity was reduced by calculating the Variance Inflation Factor Core (VIF), and highly correlated variables (> 0.85) were excluded. To obtain an "ensemble" and avoid the integration of weak models, we averaged predictions using the True Skill Statistical (TSS) method. Results showed that the ensemble model achieved the highest Area Under the Curve (AUC) scores (0.99). For B. africanus, precipitation-related variables contributed to determining the suitability for schistosomiasis. Temperature and precipitation-related variables influenced the distribution of B. globosus in all three models. Biomphalaria pfeifferi showed that Temperature Seasonality (bio4) contributed the most (47%) in all three models. According to the models, suitable areas for transmitting schistosomiasis were in the eastern regions of South Africa. Temperature and rainfall can impact the transmission and distribution of schistosomiasis in SA. The results will enable us to develop future projections for Schistosoma in SA based on climate scenarios.

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使用生态位模型对南非传播血吸虫病的蜗牛的历史分布进行建模。

血吸虫病是一种通过淡水蜗牛传播的媒介传播疾病，在卫生条件差且无法获得自来水的农村地区流行。在南非 (SA)，已知三种蜗牛可传播血吸虫病，即 Biomphalaria pfeifferi、Bulinus globosus 和 Bulinus africanus。2003 年，南澳热带气候地区的患病率预计为 70%。温度和降雨量的变化可以通过增加或减少传播血吸虫病的蜗牛的丰度和地理分布来改变其发育。本研究旨在绘制 1950 年至 2006 年南澳血吸虫病的历史分布图。蜗牛采样数据来自历史上的国家蜗牛淡水收集中心 (NFSC)。使用哥白尼气候变化服务提供的 ERA 5 再分析数据提取生物气候变量。在本研究中，我们使用了 19 个生物气候变量和 4 个土壤变量。时间聚合是在 40 年参考期内预先计算的平均气候周期，空间分辨率为 0.5° x 0.5°。通过计算方差膨胀因子核心 (VIF) 减少多重共线性，并排除高度相关的变量 (> 0.85)。为了获得“集成”并避免弱模型的集成，我们使用真实技能统计（TSS）方法对预测进行平均。结果显示，集成模型获得了最高的曲线下面积 (AUC) 分数 (0.99)。对于非洲双歧杆菌来说，与降水相关的变量有助于确定血吸虫病的适宜性。温度和降水相关变量影响了球芽孢杆菌在所有三个模型中的分布。Biomphalaria pfeifferi 显示温度季节性 (bio4) 在所有三个模型中贡献最大 (47%)。根据模型，血吸虫病的适宜传播地区是南非东部地区。温度和降雨量会影响南澳血吸虫病的传播和分布。这些结果将使我们能够根据气候情景对南澳血吸虫的未来进行预测。