02867nas a2200241   4500000000100000008004100001260003700042653002200079100001100101700001400112700001500126700001300141700001400154700001600168700001600184700001600200245012700216856009800343300001300441490000700454520215000461022001402611       2023                            d  bPublic Library of Science (PLoS)10amultidisciplinary1 aAyob N1 aBurger RP1 aBelelie MD1 aNkosi NC1 aHavenga H1 ade Necker L1 aCilliers DP1 aStauffer JR00aModelling the historical distribution of schistosomiasis-transmitting snails in South Africa using ecological niche models  uhttps://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0295149&type=printable  ae02951490 v183 a<p>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 (&gt; 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.</p>
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