Background: Assessing 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.
Methods: Here, we evaluated different statistical and machine learning methods for analyzing multiplex malaria-specific antibody response data to classify recent and historical exposure to Plasmodium falciparum and Plasmodium 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 8 P. falciparum and 6 P. vivax-specific antigens from 3 sites with varying transmission intensity.
Findings: Measurements of antibody responses and seroprevalence were consistent with the 3 sites' known endemicity status. Among the models tested, a machine learning (ML) approach (Random Forest model) using 4 serological markers (PfGLURP R2, Etramp5.Ag1, GEXP18, and PfMSP119) gave better predictions for P. falciparum recent infection in Palawan (AUC: 0.9591, CI 0.9497-0.9684) than individual antigen seropositivity. Although the ML approach did not improve P. vivax infection predictions, ML classifications confirmed the absence of recent exposure to P. falciparum and P. vivax in both Occidental Mindoro and Bataan. For predicting historical P. falciparum and P. vivax transmission, seroprevalence and seroconversion rates based on cumulative exposure markers AMA1 and MSP119 showed reliable trends in the 3 sites.
Interpretation: Our 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.
Funding: Newton Fund, Philippine Council for Health Research and Development, UK Medical Research Council.
Keywords: Analytical approaches; Machine learning; Malaria; Multiplex serology; Serosurveillance.
© 2023 Published by Elsevier Ltd.