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

Since the year 2000, controlling mosquitoes with insecticide-based interventions has led to a 37% reduction in malaria mortality globally. Nevertheless, malaria still caused 438 000 deaths in 2015, and further progress is being threatened by increasing levels of insecticide resistance in mosquito vectors. The global malaria community urgently needs new tools to monitor mosquito vector populations. Several aspects of mosquito demography and physiology are particularly crucial for planning and assessing vector control strategies. Prime amongst these are determination of mosquito vector species and age structure. Accurate species identification is required to confirm what vectors are responsible for transmission. Mosquito age is a critical determinant of their transmission potential. This is because the malaria parasite undergo a period of development within the vector before they become transmissible. Additionally, measurement of insecticide resistance status is essential to assess how effectively vectors could be targeted by current frontline control methods such as Insecticide Treated Nets (ITNs) and spraying. Unfortunately, no methods are currently available for rapid, large-scale and simultaneous measurement of these crucial mosquito vector demographic and physiological traits. This proposal aims to fill that gap by developing and validating a novel technology for high throughput, high precision surveillance of malaria vector populations in LMICs.
This project aims to develop such a tool for malaria vectors on the basis on strong existing partnerships with leading African malaria researchers, and world-class physical chemists and vector biologists in the UK. Specifically, the technology is based on the measurement of molecular signature of the mosquito cuticle, which is the outer part of the insects, to predict key traits (species, age and insecticide resistance). Indeed, the composition and structure of the mosquito cuticle, similar to the mammalian skin, changes when the organism ages, it is different in different species, and it is altered in mosquitoes that are resistant to insecticides. Here, we propose to characterize the cuticular changes associated with the traits mentioned above to then make predictions on individual mosquitoes of unknown conditions. The proposed technology is rapid and cost effective as it is based on the measurement of the light absorbed by mosquitoes, a procedure that do not require any sample preparation nor chemical reagents, and it is readily performed in few seconds by a spectrophotometer. The spectra - corresponding to the light absorbed by individual mosquitoes - will be analysed by powerful computational analysis which will enable to estimate the mosquito traits. We will follow a two-stage process starting with laboratory evaluation of mosquitoes at the University of Glasgow to build on our compelling pilot work on the use of Mid Infrared Spectroscopy (MIRS) coupled with artificial neural network (ANN). After further optimization and methods development, we will transfer this technology to two of Africa's leading malaria vector research and control institutes where it will undergo further evaluation and application within a diverse range of mosquito vector populations. We envision this will form the first steps of a pathway along which this technology can be transferred to LMICs for integration into a range of mosquito surveillance applications including programmes to eliminate malaria, and control of other mosquito-borne pathogens such as Zika and Dengue where effective solutions are desperately needed.

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 analyse 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 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.

In recent years, the global climatic changes and increased globalisation have created novel opportunities for invasive vectors to transmit new pathogens affecting humans, animals and plants. To tackle the risks of existing and emerging vector-borne diseases in a changing world, we need new efficient tools to monitor vectors and their disease transmission ability. By the development of a high-throughput method for the surveillance of malaria mosquitoes, this project will start to develop the urgently needed tools to protect our societies by the increasing Public Health and economic risks imposed by vector-borne diseases. Through the pursuit of research specific objectives and technological developments, the proposed project has potential to deliver impact within the duration of the programme through its engagement with stakeholders from academia, industry, researchers and policy makers from malaria endemic countries, vector control experts, and the public. Specifically, the vector control community, including Public Health policy makers, researchers and vector control specialists - who will be the primarily end-user of the proposed tool - will be directly informed of the project's outcomes in international meetings. Furthermore, through specific workshops knowledge exchange with researchers and post-graduate students in malaria research institutions will be established in order to build capacity to use the MIRS-ANN-based surveillance tool within vector control programmes in LMICs. Furthermore, the detailed protocol to reproduce and apply this technology to the surveillance of vector populations in endemic countries will be published in open access journals.