Reagent‑free detection  of Plasmodium falciparum malaria  infections in feld‑collected  mosquitoes using mid‑infrared  spectroscopy and machine learning

Field-derived metrics are critical for efective control of malaria, particularly in sub-Saharan Africa where the disease kills over half a million people yearly. One key metric is entomological inoculation rate, a direct measure of transmission intensities, computed as a product of human biting rates and prevalence of Plasmodium sporozoites in mosquitoes. Unfortunately, current methods for identifying infectious mosquitoes are laborious, time consuming, and may require expensive reagents that are not always readily available. Here, we demonstrate the frst feld-application of mid-infrared spectroscopy and machine learning (MIRS-ML) to swiftly and accurately detect Plasmodium falciparum sporozoites in wild-caught Anopheles funestus, a major Afro-tropical malaria vector, without requiring any laboratory reagents. We collected 7178 female An. funestus from rural Tanzanian households using CDC-light traps, then desiccated and scanned their heads and thoraces using an FT-IR spectrometer. The porozoite infections were confrmed using enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), to establish references for training supervised algorithms. The XGBoost model was used to detect sporozoite-infectious specimen, accurately predicting ELISA and PCR outcomes with 92% and 93% accuracies respectively. These fndings suggest that MIRS-ML can rapidly detect P. falciparum in feld-collected mosquitoes, with potential for enhancing surveillance in malaria-endemic regions. The technique is both fast, scanning 60–100 mosquitoes per hour, and cost-efcient, requiring no biochemical reactions and therefore no reagents. Given its previously proven capability in monitoring key entomological indicators like mosquito age, human blood index, and identities of vector species, we conclude that MIRS-ML could constitute a low-cost multi-functional toolkit for monitoring malaria risk and evaluating interventions.

Keywords

Malaria, sub-Saharan Africa, Mosquitoes

Citation

Mwanga, E.P., Kweyamba, P.A., Siria, D.J. et al. Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes using mid-infrared spectroscopy and machine learning. Sci Rep 14, 12100 (2024). https://doi.org/10.1038/s41598-024-63082-z

URI

https://hdl.handle.net/10539/38616

Collections

*School of Public Health (Research Outputs)

Full item page