An exciting post-doctoral position exists, funded by the Wellcome Trust, considering whether vaccinating at-risk populations with Meningococcal B vaccine reduces incidence and antimicrobial resistance in gonococcal infections in the UK. The role will focus on mechanistic and data-based modelling of AMR, vaccination, disease epidemiology including cost effectiveness evaluation. Further roles, subject to funding, may be available to extend work in the group on COVID-19.

The Research Associate will join a rapidly-growing infectious disease modelling group within the Department of Mathematics, working directly with Dr’s Ian Hall, Lorenzo Pellis and Alex Thompson (Centre for Health Economics), and in close and strong collaboration with epidemiology colleagues in Public Health England.

The project lies at the interface between applied mathematics, statistics and data science. The overall aim of the project is to develop, simulate and parameterise a model for Neisseria gonorrhoeae and Neisseria meningitidis circulation. Control of gonorrhoea is likely to become increasingly difficult due to widespread antibiotic resistance. While vaccines are routinely used for N. meningitidis, no vaccine is available for N. gonorrhoeae. Recent studies where meningococcal B (MenB) vaccine is given to adolescents reported a reduction in incidence rates of GC in those vaccinated, as the vaccine potentially offers some cross protection. The model created will be used to investigate the cost-effectiveness of MenB vaccine in infants, adolescents and targeted at-risk populations in reducing MenB and GC infection incidence and AMR. Building on this the potential impact in areas of low, medium and high incidence of GC infection and low and high level AMR in GC will be considered.

You will hold (or be about to complete) a PhD or equivalent in Statistics, Data Science or a closely related field. You will have a solid track record of publications relevant to the project area, and an aptitude for interdisciplinary research. A practical experience in at least one of the following areas is essential for the project: epidemic modelling, stochastic processes, uncertainty quantification or operational research.