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

Rob Deardon, Stephen P. Brooks, Bryan T. Grenfell, Matthew J. Keeling, Michael J. Tildesley, Nicholas J. Savill, Darren J. Shaw, Mark E.J. Woolhouse

Research output: Contribution to journalArticlepeer-review

88 Scopus citations


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 due 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 that can be used to estimate parameters for such large, and/or incomplete, data sets. This is done in the context of a study of the UK 2001 foot-and-mouth disease (FMD) epidemic.

Original languageEnglish (US)
Pages (from-to)239-261
Number of pages23
JournalStatistica Sinica
Issue number1
StatePublished - Jan 2010

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Bayesian inference
  • Computational methodology
  • Foot-and-mouth disease
  • Markov chain monte carlo
  • Missing data
  • Spatio-temporal epidemic modelling


Dive into the research topics of 'Inference for individual-level models of infectious diseases in large populations'. Together they form a unique fingerprint.

Cite this