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

Akira Endo^*, Edwin van Leeuwen, Marc Baguelin

^*Corresponding author for this work

Modelling Economics Unit

Research output: Contribution to journal › Article › peer-review

31 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 language	English
Article number	100363
Journal	Epidemics
Volume	29
DOIs	https://doi.org/10.1016/j.epidem.2019.100363
Publication status	Published - 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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.epidem.2019.100363

Cite this

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title = "Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers",

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",

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KW - Hidden Markov process

KW - Particle Markov-chain Monte Carlo

KW - Particle filter

KW - Sequential Monte Carlo

KW - State-space models

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

Abstract

Bibliographical note

Keywords

UN SDGs

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