AbstractThe objective of the research was to identify the most accurate models for forecasting malaria at six different sentinel sites in Uganda, using environmental and clinical data sources. We generated short-term, intermediate, and long-term forecasts of malaria prevalence at weekly intervals. The model with the most accurate forecasts varied by site and by forecasting horizon. Treatment predictors were retained in the most accurate models across all clinical sites and forecasting horizons. These results demonstrate the utility of using treatment predictors in conjunction with environmental covariates to predict malaria burden.
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