Spatial heterogeneities are frequently found to have a significant impact on the spread of disease within a country. Even in the case of widespread endemic infection, regional variation in population demographics, deprivation measures and access to healthcare resources often lead to large disparities in both infection prevalence and the subsequent impact of disease.

In all situations where an effective vaccine is available, optimal deployment strategies are critical in maximising the impact of available vaccines whilst respecting supply, deployment and cost related constraints. During the COVID-19 pandemic, the use of complex vaccine models proved effective in helping to shape a world leading vaccination campaign within the UK, though the focus here was on mass deployment and the potential benefits of spatial targeting was not explored in detail. While there is an existing precedent for spatial modelling in epidemiology, such dynamics have been largely neglected in the context of national vaccination strategies.

This project will look to address this through the construction and application of a spatial vaccine modelling framework. This will involve model development, fitting and sensitivity analysis and would expect to be highly impactful in helping to shape future national vaccination campaigns against both newly emerging and current diseases, applicable within the UK as well as within other nations across the globe.

The project will suit a graduate with a strong mathematical or statistical background who is motivated by interests in public/global health. Potential candidates should have experience in statistical analysis, inference, and modelling of deterministic and stochastic processes, with some knowledge of building epidemic models. The project will also require the ability to code in a scientific programming language (e.g. python/R/Matlab) and an aptitude for the clear interpretation and communication of results.