Competition fully funded PhD studentship (UK only). Includes Home (UK) tuition fees, a stipend paid at UKRI rates, and a bench fee allowance.

The evolution of human viral infections imposes a continuous threat for global health. The COVID-19 pandemic was characterised by substantial waves of infection resulting from increasingly infectious variants, from Alpha through to Omicron. Today, we are continuing to see high numbers of COVID-19 cases worldwide due to the rapid proliferation of variants that escape existing population immunity. Influenza outbreaks are similarly driven by viral evolution; however, Influenza evolution contrasts to COVID-19 by forming regular seasonal cycles in the two hemispheres.

This project aims to explore routes of emergence and spread of novel variants of pandemic and globally endemic pathogens. A successful project will increase our understanding of where high risk locations for variant emergence are, and the routes by which such variants subsequently spread around through world. This insight will inform key location for surveillance for the early detection of variants of concern, predict future trends of infection in globally endemic disease, and explore the use of interventions aimed at disrupting the cycle of infection driven by variant emergence. The project will be split into three parts:

Part 1: Analysing patterns of pathogen emergence for influenza and SARS-CoV-2 in existing data.

Part 2: Development of a global model for pathogen evolution and spread.

Part 3: Model exploration to aid pandemic preparedness.

The project will aim for a PhD by publication, where the expectation is that there would be at least 3 publications arising directly from the three parts of the project. Outputs will be informative for both pandemic preparedness and mitigation of endemic disease and seasonal viral pathogens.

It is expected that insights into the threat of viral evolution will be of significant interest to local public health agencies, wider scientific field, and international organisations such a WHO and BMGF.

Suitable candidates should be quantitatively orientated, with an aptitude for coding and mathematics/statistics. Experience in epidemiological modelling and/or data handling would be strongly advantageous.

For informal discussions and queries in advance of applying, please contact Dr Sam Moore; s.moore12@lancaster.ac.uk

To apply, please send your CV (max 2 pages) including the names of two referees and cover letter (outlining your interest in this PhD and qualifications) to Dr Sam Moore; s.moore12@lancaster.ac.uk