Researchers have developed a quick and cost-effective way to determine the age of malaria mosquitoes using mid-infrared spectroscopy. The new technology is vital for assessing the effectiveness of control interventions as only older mosquitoes can transmit the parasite. The scientists say that their approach could also help with other mosquito-borne and insect-borne diseases.
The researchers have combined mid-infrared spectroscopy with deep learning to develop a fast, cost-effective way to identify the species and age of three malaria-carrying mosquito species.
The researchers used data from more than 40,000 female mosquitoes of different ages from three malaria-transmitting species. The insects came from diverse genetic backgrounds and were reared in other laboratories and semi-wild conditions in East and West Africa to capture genetic and environmental variations.
The researchers initially trained the artificial intelligence (AI) model using mid-infrared spectroscopy data from the genetically varying lab-reared mosquitoes. The team then retrained it using an additional sample of mosquitoes from the semi-wild populations. The resulting model predicted the age and species of mosquitoes reared in semi-wild conditions from their mid-infrared signatures with more than 95% accuracy.
To further test the effectiveness of this “transfer-learning” approach, the researchers collected wild mosquitoes in Burkina Faso and Tanzania. They then dissected and age-assessed a sample of these and used them to retrain the AI model that was previously trained on the lab-reared mosquito populations.