A novel approach to assess malaria mosquito biological age to accelerate the implementation of vector control in endemic settings

As effective vaccines do not exist for most vector-borne diseases (VBDs), including malaria and dengue, control and prevention of these diseases rely primarily on reducing the arthropod vector populations. The future effectiveness of these public health initiatives is currently at risk, threatened by increased vector abundance following global climate warming and the spread of insecticide resistance. To tackle this emergency, there is urgent need to accelerate the evaluation of new vector control interventions. However, challenges abound. Specifically, there are no appropriate methods to measure mosquito age in vector populations, despite mosquito age being the most important factor that affects disease transmission; indeed, only vectors that live beyond the pathogen’s developmental time can transmit disease. This project will close this methodological gap by integrating physical chemistry, computing science and medical entomology and develop a rapid and cost-effective tool based on mid-infrared spectroscopy (MIRS) and convolutional neural network (CNN) analysis to determine the age of individuals from three major species of malaria vectors. This technology will be designed to work under different ecological settings, accounting for environmental-dependent ageing rates by predicting mosquito biological age. The proposed technology has the potential to revolutionize vector surveillance programmes and accelerate the elimination of malaria and other VBDs.

Project details

Surveillance
Vector Control

Apr 2022 — Mar 2024

$123,243

Burkina Faso
Tanzania