02402nas a2200253   4500000000100000008004100001260001200042653001300054653002100067653003200088653001800120653002000138100001300158700001600171700001100187700001400198700001300212700000900225245013100234856005300365490000800418520170800426022001402134       2024                            d  c01/202410aHotspots10amachine learning10aNeglected Tropical Diseases10aPublic health10aschistosomiasis1 aSinger B1 aCoulibaly J1 aPark H1 aAndrews J1 aBogoch I1 aLo N00aDevelopment of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration.  uhttps://www.pnas.org/doi/10.1073/pnas.23154631200 v1213 a<p>Schistosomiasis is a neglected tropical disease affecting over 150 million people. Hotspots of transmission-communities where infection prevalence does not decline adequately with mass drug administration-present a key challenge in eliminating schistosomiasis. Current approaches to identify hotspots require evaluation 2-5 y after a baseline survey and subsequent mass drug administration. Here, we develop statistical models to predict hotspots at baseline prior to treatment comparing three common hotspot definitions, using epidemiologic, survey-based, and remote sensing data. In a reanalysis of randomized trials in 589 communities in five endemic countries, a regression model predicts whether infection prevalence will exceed the WHO threshold of 10% in year 5 ("prevalence hotspot") with 86% sensitivity, 74% specificity, and 93% negative predictive value (NPV; assuming 30% hotspot prevalence), and a regression model for achieves 90% sensitivity, 90% specificity, and 96% NPV. A random forest model predicts whether moderate and heavy infection prevalence will exceed a public health goal of 1% in year 5 ("intensity hotspot") with 92% sensitivity, 79% specificity, and 96% NPV, and a boosted trees model for achieves 77% sensitivity, 95% specificity, and 91% NPV. Baseline prevalence is a top predictor in all models. Prediction is less accurate in countries not represented in training data and for a third hotspot definition based on relative prevalence reduction over time ("persistent hotspot"). These models may be a tool to prioritize high-risk communities for more frequent surveillance or intervention against schistosomiasis, but prediction of hotspots remains a challenge.</p>
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