Analytical approaches for antimalarial antibody responses to confirm historical and recent malaria transmission: an example from the Philippines

BackgroundAssessing the status of malaria transmission in endemic areas becomes increasingly challenging as countries approach elimination. Serology can provide robust estimates of malaria transmission intensities, and multiplex serological assays allow for simultaneous assessment of markers of recent and historical malaria exposure.MethodsHere, we evaluated different statistical and machine learning methods for analyzing multiplex malaria-specific antibody response data to classify recent and historical exposure toPlasmodium falciparumandP. vivax. To assess these methods, we utilized samples from a health-facility based survey (n=9132) in the Philippines, where we quantified antibody responses against 8P. falciparumand 6P. vivax-specific antigens from 3 sites with varying transmission intensity.FindingsMeasurements of antibody responses and seroprevalence were consistent with the 3 sites’ known endemicity status. For predictingP. falciparuminfection, a machine learning (ML) approach (Random Forest model) using 4 serological markers (PfGLURP R2, Etramp5.Ag1, GEXP18 and PfMSP119) gave better predictions for cases in Palawan (AUC: 0·9591, CI 0·9497-0·9684) than individual antigen seropositivity. Although the ML approach did not improveP. vivaxinfection predictions, ML classifications confirmed the absence of recent exposure toP. falciparumandP. vivaxin both Occidental Mindoro and Bataan. For predicting historicalP. falciparumandP. vivaxtransmission, seroprevalence and seroconversion rates based on cumulative exposure markers AMA1 and MSP119showed reliable trends in the 3 sites.InterpretationOur study emphasizes the utility of serological markers in predicting recent and historical exposure in a sub-national elimination setting, and also highlights the potential use of machine learning models using multiplex antibody responses to improve assessment of the malaria transmission status of countries aiming for elimination. This work also provides baseline antibody data for monitoring risk in malaria-endemic areas in the Philippines.FundingNewton Fund, Philippine Council for Health Research and Development, and UK Medical Research Council.