Using mathematical modelling to re-think global pneumococcal immunisation strategies. (360G-Wellcome-208812_Z_17_Z)

Despite the success of pneumococcal conjugate vaccines (PCVs) two major challenges remain. First, serotype replacement has mitigated PCV impact and pneumococci remain a predominant cause of child death. Second, PCVs are among the most expensive vaccines, which threatens sustainability in low income countries as they transition from Gavi support. The fellowship addresses those issues using a combination of epidemiological, statistical and modelling techniques and the collection of key data that leverages ongoing studies. I plan to use machine learning to unravel drivers of heterogeneity in the global serotype distribution of pneumococci, which will enable inference of pneumococcal epidemiology in settings with limited data. I will then use constrained optimisation on models that include the dynamics of serotype replacement into their predictions to inform design of more efficient PCVs that can further reduce the global pneumococcal diseases burden. I further plan to use a set of novel models to estimate who infects infants, a key knowledge gap in the assessment of reduced dose PCV schedules, an approach being pursued as a potential cost saving strategy. Phylogenetic inference in combination with mathematical models will be used to identify common transmission pathways to infants and risk factors associated with them.