Title: Bayesian individual-level infectious disease modelling: inhomogeneous mixing and an automated approach for updating infection times

Abstract: Fitting mathematical models to epidemic data is challenging because the transmission process is largely unobserved. Add into the mix that the characteristics of an individual can affect their ability to catch and transmit infectious diseases and resulting models can be both complex and computationally costly. Here we develop a model that allows us to make inference about underlying parameters that drive epidemics whilst accounting for individual-level covariates (e.g. age) and shared covariates – living in the same house, for example. The use of an adaptive MCMC algorithm featuring novel approaches to updating unknown infection/infectious times allows us to estimate model parameters and identify characteristics that affect transmission in a fashion that is computationally viable. Model performance is assessed through a variety of simulation work