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AbstractThe global elimination of lymphatic filariasis (LF) is a major focus of the World Health Organization. One key challenge is locating residual infections that can perpetuate the transmission cycle. We show how a targeted sampling strategy using predictions from a geospatial model, combining random forests and geostatistics, can improve the sampling efficiency for identifying locations with high infection prevalence. Predictions were made based on the household locations of infecteddoi:10.1038/s41598-020-77519-8 pmid:33239779 fatcat:ipke335mmzbj5ol5sw5a5w4csm