2024-25 Project (White & Sumner & Clark)

Creating evidence for novel Tuberculosis vaccine introduction

SUPERVISORY TEAM

Supervisor

Professor Richard White at LSHTM
Email: richard.white@lshtm.ac.uk

Co-Supervisor

Dr Tom Sumner at LSHTM
Email: tom.sumner@lshtm.ac.uk

Co-Supervisor

Dr Rebecca Clark at LSHTM
Email: Rebecca.Clark@lshtm.ac.uk

PROJECT SUMMARY

Project Summary

Help countries decide if and how they would introduce a new vaccine against the killer disease tuberculosis – using mathematical modelling    Despite being an ancient disease, tuberculosis (TB) remains a leading cause of death from infectious disease worldwide. A new TB vaccine, M72/AS01E, has shown considerable promise in clinical trials. Should this vaccine be approved, global and country decision makers do not know the optimal strategies to deploy it, nor what the interaction of vaccine programmes with other non-vaccine TB control efforts will be.  This knowledge is essential for decision makers considering if, and how, to introduce the vaccine.     The optimal strategies will vary substantially depending on when and how the vaccine is deployed, the country epidemiology and heath system, the planned future changes to other TB control efforts, and other factors.    This project will extend an existing state-of-the-art mathematical model of tuberculosis, developed and programmed in R, to identify optimum strategies for vaccine deployment. In particular the project will focus on investigating how best to deploy vaccines and dynamically adapt vaccine strategies in “alternative futures”, where other (non-vaccine) TB control options (e.g., new drug treatments, or better diagnostics) are introduced.    The evidence from this work will support countries in their TB vaccine introduction decision making and will be disseminated in policy briefs, publications, conference presentations and via policy networks (WHO, CTVD, Stop TB, country TB programmes).

Project Key Words

Mathematical modelling, tuberculosis, vaccines

MRC LID Themes

  • Global Health = Yes
  • Health Data Science = Yes
  • Infectious Disease = Yes
  • Translational and Implementation Research = Yes

Skills

MRC Core Skills

  • Quantitative skills = Yes
  • Interdisciplinary skills = Yes
  • Whole organism physiology = No

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

Mathematical (dynamic transmission) modelling of tuberculosis, including model parameterisation and contextualisation.   
Model inference and fitting using Bayesian methods, including but not limited to Monte Carlo simulation, history-matching and emulation, Sequential Monte Carlo methods.   
Programming, including but not limited to R.   
Familiarity with high performance computing.   
Contribution to vaccine development and implementation policy.

Routes

Which route/s is this project available for?

  • 1+4 = Yes
  • +4 = Yes

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

  • 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

Eligibility/Requirements

Particular prior educational requirements for a student undertaking this project

  • LSHTM’s standard institutional eligibility criteria for doctoral study.
  • The student should have a background in quantitative data analysis (e.g. an MSc in Epidemiology, stats, economics etc), or a mathematical background (e.g. degree in maths, physics, engineering etc).   
  • Some experience of mathematical modelling is desirable, but not essential.   
  • Prior experience in programming, specifically in R would be highly advantages but not essential.

Other useful information

  • Potential CASE conversion? = No

PROJECT IN MORE DETAIL

Scientific description of this research project

Aims and objectives   

Aim   
This PhD studentship aims to create evidence to support novel Tuberculosis vaccine introduction and implementation decision making, using mathematical modelling    

Objectives   
1. Extend country-level Mycobacterium tuberculosis transmission models to reflect new knowledge on TB natural history 
2. Establish future no-vaccine scenarios of programmatic tuberculosis management, informed by country-level stakeholders, published literature, and global political commitments to TB control. 
3. Model new tuberculosis vaccines, with varying characteristics (e.g., efficacy, durability of protection,…) and new implementation scenarios (e.g. mass vs routine immunisation, targeting by age-, nutritional status-, socioeconomic status, comorbidity, or other risk groups). 
4. Identify optimal vaccine implementation strategies, using model evidence on impact, cost-effectiveness, budget impact and other outcomes agreed in discussion with country and global policy makers   

Techniques   
The project will adapt existing state-of-the-art dynamic transmission models specified in an XML-based domain-specific format, implemented in R programming language.  Models will be calibrated using history matching/emulation or Approximate Bayesian Computation methods to fit both historical and projected TB epidemiology and demography. The project will include cost-effectiveness analyses and health economic evaluation of vaccine outcomes and the use of numerical optimisation strategies to identify optimal vaccine implementation strategies   

Datasets   
Data collection and analysis has finished for all datasets (Ethics approved in country and at LSHTM), and the data are already available to the student. All other datasets are publicly available.   

Risks and mitigation   
As the project will use existing data, there are no real potential risks to the project

Further reading

(Relevant preprints and/or open access articles)

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.
    White-Sumner-Clark Recording

MRC LID LINKS

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