Estimating the changing prevalence of molecular markers of artemisinin partial resistance in <i>Plasmodium falciparum</i> malaria in Sub-Saharan Africa

Abstract

Background Artemisinin-based combination therapies (ACTs) are the most widely used treatment for Plasmodium falciparum malaria. Kelch 13 mutations associated with artemisinin partial resistance (ART-R) have emerged in Sub-Saharan Africa (SSA) and are now reported in an increasing number of countries. ACT treatment failure rates are at risk of unprecedented increase. To summarise existing surveillance data and guide future surveillance, we produce modelled estimates of the spatiotemporal distributions of Kelch 13 and partner drug marker prevalence in SSA. Methods We develop and validate spatiotemporal statistical models, fitted within a Bayesian framework, given molecular surveillance data. We estimate the prevalence of Kelch 13 mutations that are validated or candidate markers of ART-R and the prevalence of the mutations Pfcrt-K76T, Pfmdr1-N84Y, Pfmdr1Y186F, and Pfmdr1-D1246Y, associated with selection by pressure from the ACT partner drugs amodiaquine and lumefantrine. Findings Our models reflect all existing clusters of ART-R-associated Kelch 13 mutations. We estimate the prevalence of these Kelch 13 mutations to be greater than 10% in 23% of the area of endemic malaria transmission in SSA in 2026. We also estimate that 5.8% of malaria cases in 2026 will be a!ected by a validated or a candidate ART-R marker. Our estimates of the prevalence of Pfcrt-K76T and other partner drug markers reflect sustained pressure from artemether-lumefantrine: we estimate the median prevalence of Pfcrt-76T across SSA to be 19% in 2026. Interpretation Our models allow readers to visualise variation in observed mutation prevalences and to extrapolate prevalence to regions in space and time that are not represented in surveillance data. To monitor the changing distribution of antimalarial resistance markers within the constraints of the current global health funding climate it is critical that validated, statistical frameworks are incorporated into decisionmaking workflows to make the best use of molecular surveillance data. Funding This research was funded by the European Union under the Global Health EDCTP3 Joint Undertaking (grant agreement 101103076) and the Australian National Health and Medical Research Council (APP2019093).