VectorPredictor classifies mosquito species and age bins from Mid-Infrared Spectroscopy (MIRS) spectra using machine learning models, with reported uncertainty for each result.

Our mission is to provide accessible tools for analysing mosquito spectral data, with explicit confidence information to support research and surveillance workflows.

Models analyse spectral patterns and assign class labels with associated probabilities. They learn associations between spectra and training labels — they do not directly measure age or species identity.

Our approach is rooted in the principles of data science and entomology. By exploring patterns in mosquito data, we can provide valuable insights into species distribution and age profiles.

Meet the dedicated individuals behind this project:

Principal investigators

• Simon Babayan | Glasgow | Personal | Google Scholar | ORCID
• Francesco Baldini | Glasgow | Personal | Google Scholar | ORCID
• Fredros Okumu | Glasgow | Ifakara | Personal | Google Scholar | ORCID

Co-investigators

We're here to answer your questions, listen to your feedback, and collaborate with you on further developments.