Weighted variable importance of predictor variables given by the three machine-learning models included in the model ensemble.

(A) West Africa; (B) East Africa. Stacked bars show the relative variable importance given by XGB (blue), RF (green), and BGAM (grey), weighted by the fitted weight for each model given by the Gaussian process meta-model (see text). Variables are ranked by the total height of the stacked bars across the 3 models, and the top 20 variables are shown. The original variable importance values produced by each model are given in S5 Table and S6 Table, and definitions of each predictor variable are given in S9 Table. Variable name suffixes (-1), (-2), and (-3) denote time lags of 1, 2, and 3 years, respectively. One, two, and three asterisks denote the first, second, and third principal component, respectively, for variables available on a monthly time step (see “Methods”). BGAM, boosted generalized additive model; IRS, indoor residual spraying; ITN, insecticide-treated net; PET, potential evapotranspiration; RF, random forest model; XGB, extreme gradient boosting model.