Development of a new tool for malaria mosquito surveillance to improve vector control

Malaria transmission is influenced not only by vector abundance, but as well by demographic traits such as vector species and age structure, as these influence the intensity by which the disease is transmitted. Measuring these traits and the susceptibility to insecticide in natural mosquito populations is key to implement vector control strategies. Currently, methods to measure all these traits are expensive and time consuming and cannot be combined to simultaneously measure them in individual mosquitoes. The methodology is based on the MIRS measurement of the amount of light absorbed by the mosquito cuticle. As cuticular composition changes during mosquito ageing, differs between species and is influenced by insecticide resistance status, this project will use the MIR spectra to predict these traits. Specifically, using computational analysis based on neural networks, we will analyse the complexity of the spectra variations associated with specific traits to make accurate predictions. MIRS will be used to measure different malaria mosquito species with different traits under laboratory settings to develop predictive algorithms; afterwards this project will optimize the tool incorporating spectra from natural mosquitoes collected from the field in collaboration with African malaria vector control leading institutions. The development of tool in partnerships with African researchers will directly enable the direct integration of this technology into large scale vector surveillance programmes, enabling critically important insights to assist the control of malaria vectors.

UKRI Project details

Product Development
Surveillance
Vector Control

Mar 2017 — Sep 2019

$762,480

Burkina Faso
Mozambique
United Kingdom