Last Updated: 07/08/2019
Using machine-learning and mid-infrared spectroscopy for rapid assessment of blood feeding histories and parasite infection rates in field collected malaria mosquitoes
Objectives
This project proposes to couple MIR-spectroscopy with machine-learning algorithms and to validate them for rapid assessment of blood-feeding histories and infectiousness of field-collected Anopheles arabiensis and Anopheles funestus, which dominate malaria transmission in Tanzania.
The research team will calibrate the systems to identify different vertebrate blood meals in mosquito abdomen, and Plasmodium sporozoite in heads and thoraces. This field validation will enable scale-up of MIR based approaches, thereby significantly improving surveillance-responses and intervention monitoring.
Effective surveillance and control of malaria-transmitting mosquitoes requires quantitative understanding of key biological attributes, namely: preferred blood –hosts of mosquitoes, proportions infected with parasites, survivorship, indoor/outdoor-biting behaviour and insecticide susceptibility. Currently, identifying mosquito blood meals and Plasmodium infections involves enzyme-linked immunosorbent assays (ELISA), or polymerase chain reactions (PCR), which are time-consuming, laborious and require expensive reagents. However, advances in near-infrared spectroscopy (NIR) suggest the potential for a cheaper, quicker and non-invasive alternative for predicting age and species of mosquitoes, and detecting pathogens e.g Wolbachia and Zika virus in laboratory- infected Aedes. Promisingly, mid-infrared (MIR) can provide even better accuracies since structural identities of bio-molecules are delineated at finer resolutions than in NIR bands. However, the spectroscopy-based methods have not been field-validated because entomologist lack comparative field samples of known attributes and advanced computational methods to process large spectral datasets.
Jul 2019 — Dec 2022
$134,434
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