Bayesian modelling for individual-level infectious disease models

Modelling infectious diseases has never been more important due to the global COVID-19 pandemic. Mathematical models of disease transmission are in heavy use across the world to identify the most effective interventions to prevent the spread of infection. However, to provide effective insight into the disease dynamics, mathematical models must be fit to the available data in an effective way. This is challenging because (i) complex, computational methods are the only proven way of fitting such models and (ii) the transmission process is largely unobserved. Models are instead fitted to indirect observations of transmission, such as positive tests, hospitalisations, and deaths.

The aim of this project will be to develop methods for fitting efficient infectious disease models to data. Specifically, models which build in the effect of individual-level covariate values on parameters such as susceptibility, infectiousness and vaccine protection levels. The potential applications will be to improve modelling of infectious diseases, especially emerging diseases, helping to prevent and deal with the pandemic and future pandemics. Due to the high number of parameters that such models yield, advanced computational techniques will be required. Of particular focus here will be infectious disease models in a Bayesian setting and the use of Monte Carlo methods to help draw inference. A variety of simulated and real-world datasets will be used to build and develop models.