Estimating the worst-case scenario for malaria parasite rate in sub-Saharan Africa

Abstract Malaria remains a leading cause of morbidity and mortality worldwide, with sub-Saharan Africa bearing the highest burden. Stalled progress under an inadequate budget and the expectation that funding would be further constrained [1, 2] call for an evaluation of a worst-case scenario to appreciate malaria transmission potential in the absence of interventions. We examine a scenario in which all funding for interventions ceases for several years, allowing population immunity to adjust to a new equilibrium amid a surge in transmission, while economic development, climate, and demographics remain static, which we define as the transmission niche or “baseline” prevalence. The baseline proposed here is a set of stratified PfPr 2-10 observations to select only samples with low intervention histories, preserving diverse epidemiological context. We further developed a bespoke contrastive deep learning architecture applied to 30-meter resolution images from Landsat 8 satellites, generating a detailed vector covariate set based on observable land status, which helps produce state-of-the-art malaria estimates, an improvement over all previous covariate-based models. These features were integrated into a Bayesian MCMC model with regularisation to estimate baseline PfPr 2-10 and R 0 to generate a map of malaria incidence over all of Sub-Saharan Africa. Using population data from the World Malaria Report 2024, our model estimates 422 (275–582) million cases across sub-Saharan Africa for this worst-case baseline scenario, reflecting a 131% increase compared to previous business-as-usual baseline scenario estimates [3]. Our analyses highlight the effect of long-term benefits of two decades of investments for malaria control and the critical need for sustained intervention efforts and informed policy-making to mitigate potential resurgences in malaria transmission.