Project Title: Using machine-learning and mid infrared spectroscopy for rapid assessment of blood-feeding histories and parasite infection rates in field-collected malaria mosquitoes
Project Description: Effective surveillance and control of malaria-transmitting mosquitoes require 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 involve enzyme-linked immunosorbent assays (ELISA), or polymerase chain reactions (PCR), which are time consuming, laborious and require expensive reagents. However, advances in near-n=infrared spectroscopy (NIR) suggest potential for cheaper, quicker and non-invasive alternative for predicting age and... Effective surveillance and control of malaria-transmitting mosquitoes require 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 involve enzyme-linked immunosorbent assays (ELISA), or polymerase chain reactions (PCR), which are time consuming, laborious and require expensive reagents. However, advances in near-n=infrared spectroscopy (NIR) suggest potential for 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. I propose to couple MIR-spectroscopy with machine-learning algorithms and 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. I 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.
Principal Investigator : Emmanuel Mwanga
Department Name : EHES
Time frame: (2019-07-01) - (2022-09-22)
Wellcome Trust (Normal)
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