Artificial Intelligence and Infrared Spectroscopy to accelerate Malaria vector control
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 MIRS of laboratory mosquitoes might not accurately estimate species and age in wild mosquitoes.
- Estimate ageing rates in wild mosquitoes by analyzing ecological and environmental determinants of age and species prediction accuracy of wild mosquitoes.
- Collecting and increasing training dataset to 50,000 mosquitoes.
- Using white box machine learning algorithms to select core mosquito MIRs wave numbers that predict age of known mosquitoes.
- Using selected features to train convolutional neural meet to optimize for generalizability.
- Develop an online platform for real-time analysis of mosquito MIRS data through machine learning.