VectorPredictor is dedicated to the prediction of mosquito species and age populations using cutting-edge machine learning models and Mid-Infrared Spectroscopy (MIRS). We're passionate about advancing the field of entomology and public health, and we're excited to introduce you to our technology.

Our mission is to provide cheap, fast, accurate, and efficient tools for understanding mosquito populations. By predicting species and age distributions, we aim to aid researchers, public health officials, and organisations in their efforts to combat mosquito-borne diseases and contribute to ecological studies. We believe that the power of data-driven insights can make a meaningful impact on the world.

At the heart of our web app are advanced machine learning models and MIRS technology. Our machine learning algorithms are designed to analyse MIRS datasets and make predictions with remarkable accuracy. MIRS allows us to capture unique spectral signatures from mosquitoes, enabling us to identify species and estimate age populations.

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.