Malaria antibody responses augment surveillance in low-transmission settings in the Upper River Region, the Gambia

Introduction: antigens could classify low vs. high P(Detect) populations and identify surveillance blind spots. Methods: antigens. Analyses were stratified by transmission intensity (<5% and <10% PCR prevalence). Model performance was assessed using precision-recall area under the curve (PR AUC) in held-out test data, and permutation-based variable importance identified key predictive antigens. Results: Models demonstrated strong discrimination between detectability groups, with PR AUCs of 0.92-0.93 in the <5% stratum and 0.89-0.90 in the <10% stratum using a reduced 8-antigen panel. PfSEA, PfMSP1-19, gSG6, and Etramp4.Ag2 were consistently important predictors, though rankings varied by transmission context. Individual antigen models outperformed combined immune score approaches. Discussion: Antibody profiles can identify populations where surveillance underestimates transmission due to immunity. Integrating serological data into surveillance systems may improve detection of hidden reservoirs and support more targeted malaria elimination strategies.