Title : Genomic surveillance-guided adaptive allocation significantly reduces projected malaria incidence under artemisinin resistance
Abstract:
Background: Emerging artemisinin resistance and escalating insecticide resistance threaten recent gains in malaria control across sub-Saharan Africa. Despite this growing heterogeneity in parasite genetics and transmission intensity, intervention strategies are frequently deployed using uniform national approaches. We hypothesized that integrating parasite genomic surveillance with spatially resolved transmission modeling could identify high-impact, region-specific allocation strategies that outperform standard uniform deployment.
Methods: Publicly available Plasmodium falciparum genomic datasets (n > 12,000 isolates) were integrated with WHO malaria incidence estimates across 15 high-burden countries. Resistance-associated variants, including kelch13 and additional validated loci, were mapped using geospatial clustering and hotspot detection to identify regions of elevated resistance risk. These genomic risk surfaces were incorporated into a compartmental transmission model parameterized with vector dynamics, intervention coverage (ITNs, ACTs, IRS), treatment-seeking behavior, and health-system access. Standard national strategies were compared with
genomic-guided adaptive allocation over a 10-year projection horizon. Probabilistic sensitivity analyses were conducted to evaluate robustness across plausible resistance growth and coverage scenarios.
Results: High-resistance clusters were associated with a modeled 18–27% reduction in ACT parasite clearance probability (p < 0.01) and increased rebound risk under uniform allocation. Genomic-guided adaptive deployment reduced projected malaria incidence by 21.4% (95% CI: 17.8–24.9%) compared with standard strategies and improved cost-effectiveness by 14% in DALYs averted per dollar spent. Benefits persisted across sensitivity analyses under realistic operational constraints.
Conclusions: Genomic surveillance–guided intervention allocation substantially improves projected malaria control outcomes relative to uniform national strategies. Integrating parasite genomics into malaria elimination programs provides a scalable precision-public-health framework capable of mitigating resistance-driven resurgence and accelerating durable elimination efforts.
