Publications by authors named "Sophia H Mwinyi"
Field-derived metrics are critical for effective 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.
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- Researchers studied how much Anopheles mosquitoes prefer biting humans to predict malaria risk.
- They used a new method called mid-infrared spectroscopy and machine learning to quickly check the blood meals of these mosquitoes collected in Tanzania.
- The new technique was found to be accurate and could help monitor malaria more easily and cheaply compared to traditional methods.
Background: Accurately determining the age and survival probabilities of adult mosquitoes is crucial for understanding parasite transmission, evaluating the effectiveness of control interventions and assessing disease risk in communities. This study was aimed at demonstrating the rapid identification of epidemiologically relevant age categories of Anopheles funestus, a major Afro-tropical malaria vector, through the innovative combination of infrared spectroscopy and machine learning, instead of the cumbersome practice of dissecting mosquito ovaries to estimate age based on parity status.
Methods: Anopheles funestus larvae were collected in rural south-eastern Tanzania and reared in an insectary.