2025-26 Project (Hodgson & Kucharski)

Modelling the kinetics of immunity to influenza and SARS-CoV-2 in different populations

SUPERVISORY TEAM

Supervisor

Dr David Hodgson at LSHTM
Email: david.hodgson@lshtm.ac.uk

Co-Supervisor

Prof Adam Kucharski at LSHTM
Email: adam.kucharski@lshtm.ac.uk

PROJECT SUMMARY

Project Summary

This interdisciplinary project aims to understand how humans build immunity to SARS-CoV-2 and influenza, in collaboration with immunologists and clinical researchers working across three different continents. You will combine cutting-edge dynamic mathematical models with some of the best available cohort datasets globally, using Bayesian techniques to reconstruct immune dynamics and investigate implications for epidemics and vaccination. As well as research outputs, this project will contribute to open-source analysis tools. This is an opportunity to contribute to crucial research in immunological and infectious disease analysis and modelling. Ideal candidates should have some experience in mathematical modelling, statistics, and programming (e.g. with R).

Project Key Words

Immunology, modelling, Bayesian inference, serology

MRC LID Themes

  1. Infectious Disease
  2. Health Data Science
  3. Global Health

Skills

MRC Core Skills

  1. Quantitative skills
  2. Interdisciplinary skills

Skills we expect a student to develop/acquire whilst pursuing this project

Dynamic Modeling: Gain expertise in developing and analyzing mathematical models using differential equations and stochastic processes to simulate immune responses.

Bayesian Inference: Master Bayesian techniques for parameter estimation, including proficiency in Markov Chain Monte Carlo (MCMC) and Hamiltonian Monte Carlo (HMC) methods.

Statistical Analysis: Enhance skills in structural stability and identifiability analysis, applying both theoretical and computational approaches to evaluate model performance.

R Programming: Develop advanced programming skills in R, focusing on implementing complex models and creating user-friendly software packages.

Collaboration and Communication: Strengthen collaboration skills through engagement with research groups and stakeholders, while improving scientific communication through writing and presentations.

Routes

Which route/s are available with this project?

  • 1+4 = No
  • +4 = Yes

Possible Master’s programme options identified by supervisory team for 1+4 applicants:

  • Not applicable

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, meeting – at the minimum – the institutional research degree regulations and expectations. 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). Other travel expectations and opportunities highlighted by the supervisory team are noted below.

Primary location for duration of this research degree: LSHTM, London

Travel requirements for this project: WHO collaborating centre at Doherty Institute, Melbourne, Australia.

Eligibility/Requirements

Particular prior educational requirements for a student undertaking this project

  • Minimum LSHTM institutional eligibility criteria for doctoral study.
  • Essential: Degree level or equivalent mathematics or statistics 
  • Desirable: Experience of immunology and/or virology

Other useful information

  • Potential Industrial CASE (iCASE) conversion? = No

PROJECT IN MORE DETAIL

Scientific description of this research project

1.       Project objectives:
• Develop dynamic models of B cell proliferation and differentiation to antigen exposure for SARS-CoV-2 and Influenza
• Use Bayesian techniques to fit these models to existing immunological data collected through flow cytometry and perform structural stability and identifiability analysis on these models.
• After applying models to other pathogens commit to writing a flexible R package which allows other groups to fit similar model structures.

2. Techniques to be used:
• Dynamic Modeling: Develop mathematical models that simulate B cell proliferation and differentiation in response to antigen exposure, specifically targeting SARS-CoV-2 and Influenza. This will involve differential equations and/or stochastic processes to capture the complexity of immune responses.
• Bayesian Inference: Utilise Bayesian techniques for parameter estimation, enabling the incorporation of prior knowledge and quantification of uncertainty in model predictions. Markov Chain Monte Carlo (MCMC), and Hamiltonian Monte Carlo (HMC) methods will be used to fit models to the flow cytometry and serological data.
• Structural Stability and Identifiability Analysis: Perform analyses to assess the stability of the model parameters for various model structures. This will involve both theoretical assessments and computational simulations.
• R Programming and Package Development: Implement the modelling framework in R, culminating in developing a flexible R package. This package will facilitate the application of the models to various pathogens and support user-friendly interfaces for other research groups.

3.Confirmed availability of any required databases or specialist materials:
The project will utilise several established databases and datasets and use future datasets which are currently being created from collected samples. First, immunological flow cytometry and serological data on SARS-CoV-2 vaccination will be used; this dataset is publicly available on Github. Second, we will use immunological Flow Cytometry Data and serological data for Influenza vaccination and infection, the data currently being collected from our collaborators at the WHO Collaborating Center for Influenza at the Doherty Institute, Melbourne. In addition, we will continue to deepen our relationship with existing collaborations and encourage them to collect more flow cytometry data, and/or extract immunological data from publicly available databanks such as ImmPort.

4. Potential risks to the project and plans for their mitigation:
When requiring new data, there is a risk of limited access to high-quality data or datasets that may not meet the required standards. To mitigate this, alternative data sources will be identified, and collaborations with established research groups will be pursued to secure access to robust datasets. Further, the dynamic models may become overly complex, making them difficult to analyse or fit. To address this, an iterative approach will be employed, starting with simpler models which have already been established and gradually increasing complexity as needed while continuously validating against empirical data. Finally, there may be challenges in ensuring that the R package developed is user-friendly and widely adopted. To address this, early engagement with potential users and continuous feedback will be sought throughout the development process to ensure the package meets community needs.

Further reading

Relevant preprints and/or open access articles:
(DOI = Digital Object Identifier)

Additional pre-application materials:

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.

Hodgson-Kucharski recording

MRC LID LINKS

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