2026-27 Project (Robert & Eggo & Suffel)
Modelling measles transmission risk in adults
SUPERVISORY TEAM
Supervisor
Dr Alexis Robert at LSHTM
Faculty of Epidemiology & Population Health, Department of Infectious Disease Epidemiology and Dynamics
Email: alexis.robert@lshtm.ac.uk
Co-Supervisor
Professor Rosalind Eggo at LSHTM
Faculty of Epidemiology & Population Health, Department of Infectious Disease Epidemiology and Dynamics
Email: r.eggo@lshtm.ac.uk
Co-Supervisor
Dr Anne Suffel at LSHTM
Faculty of Epidemiology & Population Health, Department of Infectious Disease Epidemiology and Dynamics
Email: anne.suffel@lshtm.ac.uk
PROJECT SUMMARY
Project Summary
Across Europe and the Americas, measles transmission was mostly interrupted between 2020 and 2022, coinciding with changes in contact behaviours during the COVID-19 pandemic. A resurgence of measles transmission has been observed since 2024, with decade-high levels of incidence reported in the UK (in 2024), the US, Canada, and Mexico (in 2025). In such settings, 25 to 50% of cases are typically reported in teenagers and adults. Capturing transmission risks among teenagers and adults is challenging: historical vaccine coverage data may not capture current susceptibility; contact patterns are different in teenagers and adults and changed after the COVID-19 pandemic; and slow waning of vaccine-induced immunity may impact future outbreak risk. We will use mathematical models and electronic health records to analyse measles surveillance data and assess measles outbreak risks in teenagers and adults.
Project Key Words
measles, vaccination, contact patterns, mathematical modelling
MRC LID Themes
- Infectious Disease
- Health Data Science
Skills
MRC Core Skills
- Quantitative skills
Skills we expect a student to develop/acquire whilst pursuing this project:
- Designing infectious disease models
- Fitting infectious disease models to data
- Generating and analysing stochastic simulations
- Programming and coding skills
- Experience handling health records
- Bayesian methods for parameter inference
- Subject-specific expertise on measles transmission
- Scientific writing and presenting
- Working as part of a team
Routes
Which route/s are available with this project?
- 1+4 = Yes
- +4 = Yes
Possible Master’s programme options identified by supervisory team for 1+4 applicants:
- LSHTM – MSc Control of Infectious Diseases
- LSHTM – MSc Epidemiology
- LSHTM – MSc Health Data Science
- LSHTM – MSc Medical Statistics
Full-time/Part-time Study
Is this project available for full-time study? Yes
Is this project available for part-time study? Yes
Location & Travel
Students funded through MRC LID are expected to work on site at their primary institution. At a minimum, all students must meet the institutional research degree regulations and expectations about onsite working and under this scheme they may be expected to work onsite (in-person) more frequently.
Students may also be required to travel for conferences (up to 3 over the duration of the studentship), and for any required training for research degree study and training. Other travel expectations and opportunities highlighted by the supervisory team are noted below.
Day-to-day work (primary location) for the duration of this research degree project will be at: LSHTM – Bloomsbury, London
Travel requirements for this project: None
Eligibility/Requirements
Particular prior educational requirements for a student undertaking this project
- Minimum standard institutional eligibility criteria for doctoral study at LSHTM
- Quantitative training in a relevant MSc. Students can take modules at LSHTM for further training.
Other useful information
- Potential Industrial CASE (iCASE) conversion? = No
PROJECT IN MORE DETAIL
Scientific description of this research project
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.
Further reading
Relevant preprints and/or open access articles:
(DOI = Digital Object Identifier)
Other pre-application materials: None
Additional information from the supervisory team
The supervisory team has provided a recording for prospective applicants who are interested in their project. This recording should be watched before any discussions begin with the supervisory team.
Robert & Eggo & Suffel Recording
MRC LID LINKS
To apply for a studentship: MRC LID How to Apply
Full list of available projects: MRC LID Projects
For more information about the DTP: MRC LID About Us