The London School of Hygiene & Tropical Medicine (LSHTM), Imperial College London and the UK Health Security Agency (UKHSA) are pleased to invite applications for a PhD studentship in real-time infectious disease modelling, as part of the NIHR funded Health Protection Research Unit (HPRU) on Health Analytics, Epidemic Modelling and Health Economics. The studentship will start in September 2026 and comes with 3.5 years of funding.
The award will cover a tax-free stipend of £ 23,805 per year and tuition fees at home rates.
The HPRU in Health Analytics and Modelling brings together three of the world’s leading groups in infectious disease analytics and modelling (at Imperial College, LSHTM and UKHSA). It will create an unparalleled environment for research degree students to thrive, being supervised by leading experts in their fields. This studentship will be based within LSHTM’s Faculty of Epidemiology and Population Health but will be jointly supervised by a team representing each of the three institutions. All three institutions are multi-disciplinary encompassing epidemiologists, data scientists, mathematical modellers, health economists and public health practitioners.
Please see the project description for the type of research involved. The exact focus of the PhD will be developed with the successful candidate and will depend on their interests and prior expertise. Applicants are asked to contact the project supervisors for an informal discussion prior to applying.
Eligibility requirements
Applicants must hold, or expect to obtain before the start of the PhD, a relevant Master’s Degree awarded with good grades, or have a combination of relevant qualifications and experience which demonstrates equivalent ability and attainment.
Applicants must meet the criteria for home fees to be eligible to apply. Your fee status is determined in accordance with the Fee Assessment Policy of LSHTM and regulations defined by the UK Government.
The PhD programme
The student will be mentored by their supervisory team made up of academics/public health specialists from each of the three institutions. They may also have a wider Advisory Committee who can help with specific issues. Students are expected to take part in the academic life of their institution and help create a strong cohort of early-career researchers across the three institutions within the HPRU. LSHTM students may join relevant Academic Centres, such as the Centre for the Mathematical Modelling of Infectious Diseases. Imperial College has the MRC Centre for Global Infectious Disease Analysis and WHO’s Collaborating Centre for Infectious Disease Modelling. Both universities have several other NIHR Health Protection Research Units. Research seminars and journal clubs in the three collaborating institutions will be open to PhD students from this scheme. Students are also able to take Master’s level study modules within either academic institution, subject to approval from their supervisors.
Support for research students’ future career development is covered through the supervision process, the transferable skills programmes and careers services within each institution. As the students will work with individuals from all three institutions they will gain excellent opportunities to network and establish professional contacts across both academia and public health. They will also have the opportunity to attend national and international conferences.
How to apply
Further information about research degree study at LSHTM as well as application guidance and a link to the portal, can be found on the School’s Research Degrees and Doctoral College pages. Applicants should submit an application for research degree study via the portal. Please write “PhD Studentships Health Analytics and Modelling HPRU” in the funding Section on the application form.
In your application, please expand on how you might address the advertised project, using a maximum of 2 pages. You may want to expand on the background information and motivation as well as outline an appropriate research methodology by which the question can be addressed.
Project
Real-Time Analysis of Household Transmission Studies for Epidemic Preparedness
Supervisory team
LSHTM: Sebastian Funk
Email: [email protected]
Imperial: Anne Cori
Email: [email protected]
UKHSA: Christopher Overton
Email: [email protected]
Household transmission studies provide detailed data on infectious disease spread, but their potential for real-time epidemic response remains underutilised. This project will develop and test analytical approaches for extracting key epidemiological parameters from household studies in real time. The student will build methods for simulated data, apply them retrospectively to COVID-19 household study data, and then use them prospectively with data from a new household study covering multiple respiratory pathogens. Methods will focus on estimating secondary attack rates, delay distributions (serial intervals, incubation periods), reproduction numbers, and transmission chains, whilst accounting for biases that arise in real-time analysis.
Key research questions include: What biases arise in standard approaches when applied in real-time? How quickly can household studies detect changes in transmission given sample size limitations? How do household-derived estimates relate to broader population patterns? The project will involve refining existing approaches, developing robust software and assessing the added value of household data for real-time situational awareness.
The work supports pandemic preparedness by creating ready-to-use analytical infrastructure alongside data collection protocols, ensuring we can rapidly generate reliable epidemiological estimates when it matters most.
- Charniga, K., Park, S. W., Akhmetzhanov, A. R., Cori, A., Dushoff, J., Funk, S., Gostic, K. M., Linton, N. M., Lison, A., Overton, C. E., Pulliam, J. R. C., Ward, T., Cauchemez, S., & Abbott, S. (2024). Best practices for estimating and reporting epidemiological delay distributions of infectious diseases using public health surveillance and healthcare data. arXiv [Stat.ME]. https://arxiv.org/abs/2405.08841
- Hart, W. S., Miller, E., Andrews, N. J., Waight, P., Maini, P. K., Funk, S.,, Thompson, R. N. Generation time of the alpha and delta SARS-CoV-2 variants: an epidemiological analysis. Lancet Infect Dis. 2022 May;22(5):603-610. doi: 10.1016/S1473-3099(22)00001-9. Epub 2022 Feb 14. PMID: 35176230; PMCID: PMC8843191. https://doi.org/10.1016/s1473-3099(22)00001-9
The deadline for applications is Thursday 30 April 23:59 (BST).
Only applications in the correct format will be considered.
Applications for the project will only be reviewed and processed after the deadline. All complete applications that are submitted before the deadline will be considered equally, regardless of submission date.