Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya

Two statistical models,a general additive model (GAM) and GAMBOOST model with boosted regression were contrasted by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to three months. The GAMBOOST model at one-month lead time had the highest predictive skill during both the training and test periods and thus can be utilized in a malaria early warning system.

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