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For each covariate, we then fit a linear model with Wg as the response and the covariate as the predictor.

We assessed the proportion of the variance of Wg explained by each covariate by computing the adjusted R2 for the covariate across all genes.

Covariate data included time-varying variables, such as temperature persistence suitability, relative humidity, and precipitation, as well as static covariates such as urban versus rural land use.

Results shown in all panels are derived from a Bayesian geostatistical model fitted to n = 27,573 PfPR survey points; n = 24,868 ITN survey points; n = 96 national survey reports of ACT coverage; n = 688 country-year reports on ITN, ACT and IRS distribution by national programs; and n = 20 environmental and socioeconomic covariate grids.

Results shown are derived from a Bayesian geostatistical model fitted to n = 27,573 PfPR survey points; n = 24,868 ITN survey points; n = 96 national survey reports of ACT coverage; n = 688 country-year reports on ITN, ACT and IRS distribution by national programs; n = 20 environmental and socioeconomic covariate grids; and n = 30 active-case detection studies reporting P. falciparum clinical incidence.