Award Title: A novel approach to assess malaria mosquito biological age to accelerate the implementation of vector control in endemic settings
Funder: The Academy of Medical Sciences

Abstract: 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. Through this project, I 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. Specifically, I will: 
1.Quantify the effect of temperature on malaria mosquito biological age
2.Develop a CNN-MIRS-based approach to determine biological age in mosquitoes. 
3.Validate the new approach on an existing dataset of 40,000 spectra from wild mosquitoes collected in Tanzania and Burkina Faso.
The proposed technology has the potential to revolutionize vector surveillance programmes and accelerate the elimination of malaria and other VBDs.

Lay Abstract: Vector-borne diseases (VBDs) such as malaria are caused by pathogens (such as viruses, bacteria or parasites) transmitted by insects or ticks and kill more than 700,000 people each year. As effective vaccines do not exist for most VBDs, the most effective way to prevent these deaths is to reduce the abundance of older insects, such as mosquitoes, as only old vectors can transmit the disease,  having lived long enough to allow the pathogen’s development to the transmissible stage. As mosquitoes are evolving and evading control strategies, new interventions need to be tested and introduced in natural mosquito populations. However, as there are no appropriate methods to measure mosquito age, control programmes often lack the evidence to establish the effectiveness of novel interventions targeted at preventing disease transmission. This project will seek to develop a new technology to measure mosquito age by combining different research disciplines. Specifically, we will rear mosquitoes of different ages under temperature-controlled conditions in the laboratory, then estimate their age using mid-infrared spectroscopy, which is a technique that allows to rapidly measure the chemical composition by quantifying the light absorbed. Finally, we will use methods from artificial intelligence to identify the predictive relationship between mosquito age and their biochemical composition. Ultimately, this project will have positive impacts both on public health in low- and middle-income countries by supporting vector control programmes, and in other areas, such as in Europe, where emerging VBDs pose new health risks, by allowing rapid screening of vector populations to prevent disease transmission.

Research Activity Codes: 3.2 INTERVENTIONS TO ALTER PHYSICAL AND BIOLOGICAL ENVIRONMENTAL RISKS
Health Categories: INFECTION