AI-MIRS: An online Platform for Malaria Vector Surveillance in Africa using Artificial Intelligence

Objectives

This project will develop a novel technology to quantify the efficacy of any control intervention targeted at malaria mosquitoes, by combining artificial intelligence and infrared-spectroscopy to obtain real-time information on mosquito populations and their disease transmission potential. However, genetic and ecological factors can affect the composition of mosquito cuticle in unexpected ways, and demographic predictions based on MMX InfraRed Spectrometry (MIRS) of laboratory mosquitoes might not accurately estimate species and age in wild mosquitoes. This grant will support all the activities and cover some personnel costs.

Specific aims:

• Aim 1. Establish best practice to estimate ageing rates in wild mosquitoes: analyze ecological and environmental determinants of age and species prediction accuracy of wild mosquitoes to optimize MIRS prediction performance and generalisability.
• Aim 2. Develop an online platform for real-time analysis of spectral data for malaria mosquito surveillance: develop a user-friendly web application to obtain a real-time analysis of mosquito infrared spectra through machine learning; this platform will also allow users to contribute data for further optimization of the machine learning algorithms.
• Aim 3: Training African scientists on machine learning.