Measles transmission dynamics have shifted in the last decade. After an interruption of transmission during COVID, the United Kingdom and the EU/EEA reported a large resurgence in 2024, while Canada, the United States and Mexico all reported decade-high number of cases in 2025. Measles is perceived as a childhood disease, but in Europe, East Asia and the Americas, 25 to 50% of cases observed since 2015 have occurred in older teenagers, or adults. Large outbreaks affecting adults are also expected in future: serological surveys (measuring antibody concentration in various age groups) highlight drops among some vaccinated adults, while modelling studies have shown a very slow waning rate from vaccine data. Under-vaccinated cohorts (for instance individuals born in late 1990s-early 2000s in England) may also be at high risk of infection. Such an increase in age of infection creates challenges, making measles outbreaks harder to anticipate and control. For instance, decrease of immunity in adults may lead to new transmission settings (workplaces, universities..) that would require appropriate control measures. However, teenagers and young adults do not follow the same contact patterns as children, so the vaccination thresholds needed to mitigate transmission may be different in these age groups, and transmission risk may stem from intergenerational contact instead of workplace transmission. Further, due to internal and external migration, historical vaccine coverage may not be representative of outbreak risk in adults, so identifying pockets of susceptibility in adult populations is challenging. This PhD project will identify hypotheses for the future transmission dynamics of measles in settings with high vaccine coverage, with the focus determined in collaboration between the student and supervisors. We will then design novel models, integrating a range of data sources, to address a hypothesis and fit these models to available data. The overarching aim is to use contact and serology surveys, along with vaccine and case data, to evaluate the risks of measles transmission among adults in future years. Ultimately, the outputs of the project will be used to develop control measures adapted to future measles transmission dynamics.

Objectives
Evaluate measles outbreak risks among teenagers and adults in settings where measles is not endemic, identify vulnerable pockets in older populations.

Techniques
Hypothesis generation and testing. Mathematical modelling of virus transmission. Fitting models to observations using Bayesian methods, simulation studies from observed data and mechanistic models.

Data access
The student will use a mix of publicly available data (on vaccine coverage, contact surveys and serology), data collected from ECDC, and Electronic Health Records data.

Risks and mitigation
Data quality may not be sufficient. In this case, we will seek data sharing permission for more granular data from ECDC, UKHSA, and OpenSafely.