The London School of Hygiene & Tropical Medicine (LSHTM), Imperial College London and the UK Health Security Agency (UKHSA) are pleased to invite applications for two PhD studentships 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 studentships will start in April or September 2026 and come with 3.5 years of funding.
The awards will cover a tax-free stipend of £22,780 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. These two studentships 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 descriptions for the type of research that the projects involve. The exact focus of each 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
Students 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.
Clearly identify the specific project (or projects) that interests you from the list of projects provided. In your application, please expand on how you might address your chosen research 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 titles
- Improving infectious disease forecasting through novel data integration and ensemble methods
Supervisory team
LSHTM:
Email: [email protected]
Imperial:
Email: [email protected]
UKHSA:
Email: [email protected]
Brief description of project / theme
Infectious disease forecasting plays a critical role in outbreak response, guiding public health interventions and resource allocation. However, many existing forecasting models rely on oversimplified assumptions, such as constant reproduction numbers (Rt), which limit their utility for long-term predictions. Increasingly, diverse data sources including wastewater surveillance, genomic data, healthcare-seeking behaviour, and mobility patterns, offer opportunities to improve forecast accuracy and robustness, yet their systematic integration into forecasting frameworks remains underexplored. Meanwhile, advances in machine learning (ML) and artificial intelligence (AI) offer new approaches to pattern recognition and prediction that complement traditional mechanistic models.
This project seeks to improve infectious disease forecasting by addressing three interconnected areas. First, developing models that move beyond stationary assumptions, enabling forecasts to better account for dynamic changes in disease transmission. Second, combining signals from multiple data streams to better account for complementary signals and biases in heterogeneous data sources. Third, designing evaluation frameworks tailored to decision-making, focusing on practical metrics such as outbreak peak timing, magnitude, and critical thresholds.
The research will be applied to case studies including COVID-19, mpox, influenza and other infectious diseases, ensuring the methods are rigorously tested and broadly applicable. The project will involve developing flexible tools that can be readily deployed in future epidemics, with potential to improve the utility and reliability of infectious disease forecasts for public health decision-making..
- Sherratt K et al. (2023) Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations. eLife https://doi.org/10.7554/eLife.81916
- Bosse NI, Abbott S, Cori A, van Leeuwen E, Bracher J, Funk S (2023) Scoring epidemiological forecasts on transformed scales. PLoS Comput Biol 19(8): e1011393. https://doi.org/10.1371/journal.pcbi.1011393
The role of the different institutions in this collaborative project
The student will be supervised by a team of supervisors representing all participating institutions and part of a team of researchers that spans the institutions. The Imperial College and LSHTM supervisors will bring the substantial methodological expertise and breadth of their institution as well as experience in bringing these to bear on public health problems. UKHSA will bring the UK-specific public health focus and ensure the project is firmly grounded in the application to data and problems as they are present in the UK.
Particular prior educational requirements and skills for a student undertaking this project
A postgraduate degree, ideally in a quantitative subject (e.g. Biostatistics, Bioinformatics, Mathematics, Statistics, Computer Science or Physics) or a related discipline (e.g. Epidemiology or Biology) with a strong quantitative element either awarded or imminent or equivalent training. Also some coding experience, ideally in R, Julia or similar languages.
Skills we expect a student to develop/acquire whilst pursuing this project
Insights into the application of quantitative techniques in public health contexts, specifically advanced analytics applied in epidemiological contexts; as well as broader understanding of forecasting and forecast evaluation.
- Real-time analysis of household transmission studies for epidemic preparedness
Supervisory team
LSHTM:
Email: [email protected]
Imperial:
Email: [email protected]
UKHSA:
Email: [email protected]
Brief description of project / theme
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 role of the different institutions in this collaborative project
The student will be supervised by a team of supervisors representing all participating institutions and part of a team of researchers that spans the institutions. The Imperial College and LSHTM supervisors will bring the substantial methodological expertise and breadth of their institution as well as experience in bringing these to bear on public health problems. UKHSA will bring the UK-specific public health focus and ensure the project is firmly grounded in the application to data and problems as they are present in the UK.
Particular prior educational requirements and skills for a student undertaking this project
A postgraduate degree, ideally in a quantitative subject (e.g. Biostatistics, Bioinformatics, Mathematics, Statistics, Computer Science or Physics) or a related discipline (e.g. Epidemiology or Biology) with a strong quantitative element either awarded or imminent or equivalent training. Also some coding experience, ideally in R, Julia, or similar languages.
Skills we expect a student to develop/acquire whilst pursuing this project
Insights into the application of quantitative techniques in public health contexts, specifically advanced analytics applied in epidemiological contexts; inference with mathematical models applied to infectious disease data sets.
Applications for these projects 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.
Only applications in the correct format will be considered.
The deadline for applications is 23:59 (GMT) 12 January 2026.