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Supporting elimination of lymphatic filariasis in Samoa by predicting locations of residual infection using machine learning and geostatistics

Mayfield, Helen; Sturrock, Hugh; Arnold, Benjamin F.; Andrade-Pacheco, Ricardo; Kearns, Therese; Graves, Patricia; Naseri, Take; Thomsen, Robert; Gass, Katherine; Lau, Colleen


The 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 infected persons...[Show more]

CollectionsANU Research Publications
Date published: 2020
Type: Journal article
Source: Scientific Reports
DOI: 10.1038/s41598-020-77519-8
Access Rights: Open Access


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