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

Akira Endo; Edwin van Leeuwen; Marc Baguelin

doi:10.1016/j.epidem.2019.100363

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

Epidemics. 2019 Dec:29:100363. doi: 10.1016/j.epidem.2019.100363. Epub 2019 Oct 3.

Authors

Akira Endo¹, Edwin van Leeuwen², Marc Baguelin³

Affiliations

¹ London School of Hygiene & Tropical Medicine, London, United Kingdom. Electronic address: [email protected].
² Public Health England, London, United Kingdom.
³ London School of Hygiene & Tropical Medicine, London, United Kingdom; Public Health England, London, United Kingdom; Imperial College, London, United Kingdom.

PMID: 31587877
DOI: 10.1016/j.epidem.2019.100363

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.

Keywords: Hidden Markov process; Particle Markov-chain Monte Carlo; Particle filter; Sequential Monte Carlo; State-space models.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Communicable Diseases / epidemiology*
Communicable Diseases / transmission*
Humans
Markov Chains*
Models, Statistical*
Monte Carlo Method*