Profile
Project title: The Inference of Demographic Parameters from Anopheline Mosquito Genomic Data using Artificial Intelligence
Summary: Understanding the genetic dynamics and migration patterns of vector populations is crucial for effective control strategies against vector-borne diseases. Some of these vector-borne diseases cause many deaths yearly. For instance, malaria causes the deaths of hundreds of thousands every year in sub-Saharan Africa.
Genomic surveillance efforts are hindered by the emergence and spread of insecticide or pesticide resistance in these vectors. The extent to which resistance mutations could spread across an entire continent remains unclear.
This project aims to address this problem by employing artificial intelligence to infer notable local parameters of the recent evolution of Anopheline mosquito populations, including temporal changes in population sizes and migration rates across some African villages.
The expected outcome of our demographic model, which allows for selections of different evolutionary scenarios, would be to infer gene flow at a local scale and make predictions about the geographical spread of insecticide-resistance mutations on the continent. Predictions would inform strategies for molecular monitoring of insecticide-resistance loci and aid in designing intervention tools for suppressing vector populations.
In summary, this project would provide a computational approach to understanding genetic resistance migration among mosquito populations, as well as contributing to the development of innovative vector control strategies - in support of the ongoing efforts to combat malaria and other vector-borne diseases.