Project: Inferring superspreading events in infectious outbreaks using pathogen genome data
This project will use known outbreaks of SARS-CoV-2 and HIV to design epidemiological models that can identify the extent to which infectious spread is driven by superspreading individuals or groups of individuals. The techniques will involve Bayesian phylodynamics analyses, with an option for deep learning methods.
We analyse genome sequence data from infectious pathogens to understand their evolution and spread. Our work combines knowledge in genomics, epidemiology, evolutionary biology and statistics. Although we rely on genome sequence data, the nature of this work is entirely computational (dry lab).
Duchene Group Current Projects
PhD/MPhil, Master of Biomedical Science