Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

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.

Supervisors

External supervisors:

Dr Liam Brierley (tertiary; University of Glasgow)

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