Mathematical modelling to improve the efficiency of vaccine development pipelines.

Vaccines are one of the major successes of public health, with modern laboratory techniques allowing the development of vaccines against a wide range of pathogens and speeding their development. Clinical trials (phases III and IV) are often costly and time consuming, acting as a bottleneck to the rapid deployment of vaccines. My project will consider how epidemiological models can be used to enhance the vaccine development pipeline, using five infections as motivating and well-understood case studies: SARS-CoV-2, respiratory syncytial virus, Rift Valley fever, Lassa fever and yellow fever. In this project I will develop a suite of sophisticated models, based on the known epidemiology of the infections and using early immunological data as strong priors on vaccine behaviour. These models will feed into pre-trial quantification of the infection dynamics, assessment of effective and efficient phase III trial design, and evaluation of the impact of deployment within the population. This will be supported by researchers working in vaccine trials and deployment within Kenya and across Africa.