Project Description
Vaccination has made an enormous contribution to global health. Unfortunately, the UK and many other countries with successful immunisation programmes, are experiencing concerning outbreaks of disease because of declines in vaccine coverage. In England in 2018-19, coverage declined in all routine childhood vaccinations compared with the previous year, and in 2019 the UK lost its World Health Organization measles-free status. National Institute for Health and Care Excellence (NICE) guidance recommends that research should explore effective ways of increasing immunisation amongst population groups at risk of being under-immunised.

This project will: identify how unvaccinated individuals connect to each other within communities to form networks capable of sustaining outbreaks; improve outbreak prediction; identify optimal ways to target vaccination to reduce infection risk and outbreak occurrence. This will be achieved through modelling and secondary analysis of data, integrating information from a range of sources including vaccination coverage, social contact information and fine-scale socio-demographic information (for example, data available through NHS Digital, Office for National Statistics, the National Pupil Database, and Census data).

Detailed models of households, schools and healthcare service use will be combined with social contact survey and vaccination information to model the social networks of susceptible individuals arising from heterogeneous vaccination coverage. Within England, some groups are at a higher risk of being under-immunised, including traveller communities, migrants, and children in care. This project aims to address important questions about the susceptibility of these hard-to-reach groups to disease outbreaks, and explore how their interaction within the population impacts disease dynamics. Finally, the project will explore the effectiveness of difference vaccination strategies, updating these models as new information becomes available, no new data will need to be collected to support this study. This project underpins a programme of work, with a blueprint that is transferable to other settings (developing countries) and could be supported by future funding applications.

This studentship would suit a candidate with a computational and mathematical background, who wishes to address important challenges in epidemiology and public health.