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 journalArticle

57 Scopus citations

Abstract

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
Volume20
Issue number1
StatePublished - Jan 1 2010

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

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

Fingerprint 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

    Deardon, R., Brooks, S. P., Grenfell, B. T., Keeling, M. J., Tildesley, M. J., Savill, N. J., Shaw, D. J., & Woolhouse, M. E. J. (2010). Inference for individual-level models of infectious diseases in large populations. Statistica Sinica, 20(1), 239-261.