VectorPredictor classifies mid-infrared (MIRS) spectra from disease vectors: Anopheles age and species labels, and a Simulium PCR pre-screen (Onchocerca-consistent signal for confirmatory qPCR), with reported uncertainty for each result.

Our mission is to provide accessible tools for analysing vector 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, species identity, or infection.

Our approach is rooted in the principles of data science and entomology. By exploring patterns in vector spectra, we can provide classifications that may inform research on Anopheles age and species and a Simulium PCR pre-screen.

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.