A new tuberculosis vaccine called M72/AS01E has shown strong results in clinical trials, but no one yet knows the best way to roll it out. This project uses mathematical modelling to help countries decide if, when, and how to introduce the vaccine. The problem is that optimal deployment strategies depend heavily on a country’s existing TB burden, its health system, and what other TB control measures—such as new drugs or better diagnostics—might also be introduced in the coming years. Without this knowledge, decision-makers at the World Health Organization and national TB programmes risk wasting resources or missing opportunities to save lives. The model simulates alternative futures: different vaccine schedules, target age groups, and combinations with other interventions, under varying socio-economic conditions. If this research succeeds, it will produce evidence that directly informs policy briefs and global guidance. The practical impact is on public health infrastructure—specifically, how vaccines are prioritised, procured, and delivered within national immunisation programmes. The work does not develop a new vaccine or run a clinical trial; it provides the quantitative framework needed to turn promising trial results into effective, real-world vaccination strategies.
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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 the introduction with other vaccine programmes and other non-vaccine TB control efforts will be. This knowledge is essential for decision makers considering if, how, and when to introduce the vaccine. Optimal strategies will vary substantially depending on when and how the vaccine is deployed, the country's epidemiology and heath system, the planned future changes to other TB control efforts, and other factors. Using mathematical modelling, this project will therefore help countries to decide which implementation strategies to use to introduce a novel vaccine against the killer disease tuberculosis - or whether to consider introduction at all. 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 vaccines against other diseases and non-vaccine TB control options (e.g., new drug treatments, or better diagnostics) are also introduced in different combinations and to different target groups. Simulating alternative scenarios can also yield an understanding of how variations in socio-economic conditions affect the expected success of the potential vaccination programme versus baseline scenarios of either no vaccination or BCG-only vaccination, which is the old vaccine currently in use. Applying advanced statistical methods to map out more the more nebulous, approximate and shifting nature of real-world scenarios offers the opportunity to develop both my quantitative and interdisciplinary skills for translational purposes with practical implications. 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).
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