Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers

Akira Endo*, Edwin Van Leeuwen, Marc Baguelin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

The particle Markov-chain Monte Carlo (PMCMC) method is a powerful tool to efficiently explore high-dimensional parameter space using time-series data. We illustrate an overall picture of PMCMC with minimal but sufficient theoretical background to support the readers in the field of biomedical/health science to apply PMCMC to their studies. Some working examples of PMCMC applied to infectious disease dynamic models are presented with R code.

Original languageEnglish
Article number100363
JournalEpidemics
Volume29
DOIs
Publication statusPublished - Dec 2019

Bibliographical note

Funding Information:
We thank Naomi R Waterlow for proofreading the manuscript. AE receives financial support fromThe Nakajima Foundation . The authors thank the UK National Institute for Health Research Health Protection Research Unit ( NIHR HPRU ) in Modelling Methodology at Imperial College London in partnership with Public Health England (PHE) for funding (grant HPRU-2012-10080 ). EvL also acknowledges the NIHR HPRU Respiratory Infections for funding. The funders had no role in conceptualisation, preparation of the manuscript or decision to publish. The authors declare that no competing interest exists.

Publisher Copyright:
© 2019 The Authors

Keywords

  • Hidden Markov process
  • Particle Markov-chain Monte Carlo
  • Particle filter
  • Sequential Monte Carlo
  • State-space models

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