Malaria importation and the role of agriculture in local transmission in Eswatini (MIRA)

About This Grant

Project Summary/Abstract As malaria transmission declines, an increasingly large proportion of the parasite reservoir is imported, with transmission persisting in specific sub-populations with high exposure to malaria and barriers to accessing and utilizing malaria prevention. Epidemiological data alone are unable to establish transmission links between infections or distinguish those that arise from importation events, posing a key barrier to measuring progress toward elimination targets. The long-term goal of this proposal is to leverage the confluence of cutting-edge molecular methods, genomic data, and novel analytic approaches to improve malaria case classification, identify drivers of residual transmission, and understand the introduction and spread of antimalarial drug resistance mutations. The overall objective is to quantify the contribution of infections imported by agricultural populations—including those carrying antimalarial drug resistance markers—to transmission in Eswatini. The central hypothesis is that imported infections in agricultural populations disproportionately contribute to ongoing malaria transmission and the spread of antimalarial drug resistance mutations. The hypothesis will be tested by pursuing two specific aims: (1) To improve case classification using high-resolution genomic data. (2) To characterize transmission dynamics and patterns of drug resistance markers in relation to imported cases within agricultural populations. To achieve these aims, existing and new high-resolution parasite genomic data will be analyzed with epidemiological data from samples collected through complementary studies conducted in Eswatini between 2012-2017 and 2023. For the latter, contemporaneous genomic data from Mozambique in 2023 will be used as a source population to assess genomic connectivity and improve genomic case classification models. Genomic transmission networks will be constructed for each dataset in addition to probabilistic models of importation based on travel history. Temporal, spatial, and demographic characteristics of highly related clusters will be quantified, and epidemiological metrics will be inferred from reconstructed networks. Findings are expected to shed light on the contribution of malaria importation and agricultural work in ongoing transmission and detection of parasites harboring antimalarial drug resistance markers in Eswatini and serve as proof of concept for innovative genomic transmission network methods for case classification and estimates of transmission between subpopulations. These contributions will be significant in providing critical evidence of the role of cross-border movement and agricultural populations in maintaining transmission and spread of antimalarial drug resistance markers in Eswatini. In addition to generating key metrics to inform decision-making in Eswatini, findings will support the broader use of genomic data and transmission network models for case classification and drug resistance surveillance. Results will be used to develop future R01 clinical trials focused on targeted active surveillance and intervention strategies at agricultural worksites and inform strategies for global surveillance of resistant parasites that pose an immediate threat to U.S. travelers.