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Developing within-host and population-scale models for optimising vaccination strategies

Lead supervisor: Professor Robin Thompson

Co-supervisor: Dr Will Hart

Commercial partner: GSK

 

 

Infectious diseases are responsible for substantial morbidity and mortality around the world. For many viruses, a key intervention is vaccination, which reduces the chance that individuals become infected and limits the negative consequences of breakthrough infections. However, optimisation of vaccine development and deployment is complex, with important challenges including determining the optimal vaccine dose and prime-boost intervals and deciding which individuals in the population to vaccinate first. Mathematical modelling can be used to inform this decision making, enabling the efficacy of vaccination to be improved.

 

In this project, we will consider how vaccine dosing can be optimised. In general, including a higher dose in a vaccine is more protective. However, high doses are costly and can be associated with an increased risk of negative side effects, as well as reduced immunogenicity, depending on the dose response. Currently, vaccine doses are chosen so that individual immune responses to vaccination are “high enough”. In other words, the vaccine dose is chosen to be as low as possible while still generating a substantial immune response in individuals tested in clinical trials.

 

The core aim of this project is to develop a multi-scale mathematical modelling framework to test whether such a within-host dose optimisation strategy for determining the vaccine dose is really optimal at the population level. For example, if a slightly higher dose was chosen, would the potential benefit (in terms of the number of cases averted in the population) outweigh the costs associated with additional vaccination side effects?

 

Key mathematical approaches that will be used in this project include stochastic and deterministic transmission modelling, Bayesian parameter inference and numerical solution of ODEs, among other techniques. This project also involves computer programming (in a language of the student’s choice; other members of Prof. Thompson’s group use Python, Matlab, R or Julia).

 

This project has the potential to generate results with substantial positive real-world impact; this impact is expected to be realised through the collaboration with GSK.

 

Apply using course: DPhil in Mathematics

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