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

70 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

Publisher Copyright:
© 2019 The Authors

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

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

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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.",

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author = "Akira Endo and \{van Leeuwen\}, Edwin and Marc Baguelin",

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doi = "10.1016/j.epidem.2019.100363",

language = "English",

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AU - Baguelin, Marc

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N2 - 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.

AB - 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.

KW - Hidden Markov process

KW - Particle Markov-chain Monte Carlo

KW - Particle filter

KW - Sequential Monte Carlo

KW - State-space models

UR - https://www.scopus.com/pages/publications/85072761527

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SN - 1755-4365

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JO - Epidemics

JF - Epidemics

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ER -

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

Abstract

Bibliographical note

UN SDGs

Keywords

Access to Document

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