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. Here we propose to develop a rapid and cost effective tool based on mid-infrared spectroscopy (MIRS) analysis to simultaneously determine these traits in malaria vectors to facilitate large scale surveillance of wild populations. Specifically, we aim to develop this technology to determine:
1- The species and age
2- Insecticide resistance status
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, we will use the MIR spectra to predict these traits. Specifically, using computational analysis based on neural networks, we will analyze the complexity of the spectra variations associated with specific traits to make accurate predictions. We will use MIRS to measure different malaria mosquito species with different traits under laboratory settings to develop predictive algorithms; afterwards we will optimize the tool incorporating spectra from natural mosquitoes collected from the field. The development of tool 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.