02121nas a2200265   4500000000100000008004100001260001200042653001800054100001500072700001500087700001300102700002200115700001300137700001300150700001300163700001400176700001200190700001100202245014800213856009000361300001000451490000700461520137300468022001401841       2020                            d  c01/202010aGeostatistics1 aMayfield H1 aSturrock H1 aArnold B1 aAndrade-Pacheco R1 aKearns T1 aGraves P1 aNaseri T1 aThomsen R1 aGass KM1 aLau CL00aSupporting elimination of lymphatic filariasis in Samoa by predicting locations of residual infection using machine learning and geostatistics.  uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689447/pdf/41598_2020_Article_77519.pdf  a205700 v103 a<p>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 identified from previous surveys, and environmental variables relevant to mosquito density. Results show that targeting sampling using model predictions would have allowed 52% of infections to be identified by sampling just 17.7% of households. The odds ratio for identifying an infected individual in a household at a predicted high risk compared to a predicted low risk location was 10.2 (95% CI 4.2-22.8). This study provides evidence that a 'one size fits all' approach is unlikely to yield optimal results when making programmatic decisions based on model predictions. Instead, model assumptions and definitions should be tailored to each situation based on the objective of the surveillance program. When predictions are used in the context of the program objectives, they can result in a dramatic improvement in the efficiency of locating infected individuals.</p>  a2045-2322