Inference for individual-level models of infectious diseases in large populations

Individual Level Models (ILMs), a new class of models, are being applied to infectious epidemic data to aid in the understanding of the spatio-temporal dynamics of infectious diseases These models are highly flexible and intuitive: and can be parameterised under a Bayesian framework via Markov chain Monte Carlo (MCMC) methods Unfortunately, this parameterisation can be difficult to implement clue to intense computational requirements when calculating the full posterior for large, or even moderately large, susceptible populations, or when missing data are present Here we detail a methodology v that can be used to estimate parameters for such large, and/or incomplete, data. sets This is clone in the context of a study of the UK 2001 foot-and-mouth disease (FMD) epidemic