The growing deluge of data available at the molecular scale can (and will) revolutionise evolutionary biology, but also the health sector. As a transient leave from the French administration, I joined Olivier Gascuel’s group of evolutionary bioinformatics as a sabbatical post-doc to discover and take part in this scientific adventure.
As part of this post-doc, I will contribute to the development and implementation of “ABC-AI-driven” phylodynamics methods to track epidemic dynamics at a large scale and inform health policies. Applications will be focused on key pathogens such as HIV and Ebola.
Phylodynamics approaches correspond to a set of statistical inference techniques where genomes of quickly evolving pathogens such as RNA viruses are used to trace back epidemic dynamics by integrating the genetic correlations among the strains of infected cases.
Phylodynamics risk predictions based on Approximate Bayesian Computations (ABC) coupled with Artificial Intelligence (AI) techniques allow us to apply phylodynamics approaches to expert models of arbitrary complexity and account for heterogeneous data: incidence, genetic, behavioural…