Combining machine-learning algorithms and infectious diseases epidemiology in zoonotic disease surveillance and pandemic response

This DPhil project will combine application of machine-learning algorithms (MLAs) with detailed agent-based modelling to untangle the zoonotic potential of different avian-influenza strains and evaluate different interventions to curb their spread among humans. The overall aim of the DPhil work is to combine construction of novel machine learning algorithms for phylogeny data with the development of a suite of ABMs across the viral strains identified by the MLAs, taking account of the different strains’ characteristics, different within-host viral dynamics and evolution, the correct transmission networks, across different settings and target populations.

External supervisors:

Dr Liam Brierley (tertiary; University of Glasgow)

Dr Lorenzo Cattarino (industry supervisor; UK Health Security Agency)