A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation

Associated organisational units

Electronic data

mainR1
Rights statement: This is the author’s version of a work that was accepted for publication in Mathematical Biosciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Mathematical Biosciences, 287, 2017 DOI: 10.1016/j.mbs.2016.07.001
Accepted author manuscript, 342 KB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Text available via DOI:

https://doi.org/10.1016/j.mbs.2016.07.001
Final published version

Keywords

Bayesian inference, Epidemics, Stochastic epidemic models, Approximate Bayesian Computation, Population Monte Carlo

View graph of relations

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation. / Kypraios, Theodore; Neal, Peter John; Prangle, Dennis.
In: Mathematical Biosciences, Vol. 287, 05.2017, p. 42-53.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{431b5c27374d4a9199140ef550511525,

title = "A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation",

abstract = "Likelihood-based inference for disease outbreak data can be very challenging due to the inherent dependence of the data and the fact that they are usually incomplete. In this paper we review recent Approximate Bayesian Computation (ABC) methods for the analysis of such data by fitting to them stochastic epidemic models without having to calculate the likelihood of the observed data. We consider both non-temporal and temporal-data and illustrate the methods with a number of examples featuring different models and datasets. In addition, we present extensions to existing algorithms which are easy to implement and provide an improvement to the existing methodology. Finally, we provide R code to implement the algorithms presented in the paper.",

keywords = "Bayesian inference, Epidemics, Stochastic epidemic models, Approximate Bayesian Computation, Population Monte Carlo",

author = "Theodore Kypraios and Neal, {Peter John} and Dennis Prangle",

note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Mathematical Biosciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Mathematical Biosciences, 287, 2017 DOI: 10.1016/j.mbs.2016.07.001",

year = "2017",

month = may,

doi = "10.1016/j.mbs.2016.07.001",

language = "English",

volume = "287",

pages = "42--53",

journal = "Mathematical Biosciences",

issn = "0025-5564",

publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation

AU - Kypraios, Theodore

AU - Neal, Peter John

AU - Prangle, Dennis

N1 - This is the author’s version of a work that was accepted for publication in Mathematical Biosciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Mathematical Biosciences, 287, 2017 DOI: 10.1016/j.mbs.2016.07.001

PY - 2017/5

Y1 - 2017/5

N2 - Likelihood-based inference for disease outbreak data can be very challenging due to the inherent dependence of the data and the fact that they are usually incomplete. In this paper we review recent Approximate Bayesian Computation (ABC) methods for the analysis of such data by fitting to them stochastic epidemic models without having to calculate the likelihood of the observed data. We consider both non-temporal and temporal-data and illustrate the methods with a number of examples featuring different models and datasets. In addition, we present extensions to existing algorithms which are easy to implement and provide an improvement to the existing methodology. Finally, we provide R code to implement the algorithms presented in the paper.

AB - Likelihood-based inference for disease outbreak data can be very challenging due to the inherent dependence of the data and the fact that they are usually incomplete. In this paper we review recent Approximate Bayesian Computation (ABC) methods for the analysis of such data by fitting to them stochastic epidemic models without having to calculate the likelihood of the observed data. We consider both non-temporal and temporal-data and illustrate the methods with a number of examples featuring different models and datasets. In addition, we present extensions to existing algorithms which are easy to implement and provide an improvement to the existing methodology. Finally, we provide R code to implement the algorithms presented in the paper.

KW - Bayesian inference

KW - Epidemics

KW - Stochastic epidemic models

KW - Approximate Bayesian Computation

KW - Population Monte Carlo

U2 - 10.1016/j.mbs.2016.07.001

DO - 10.1016/j.mbs.2016.07.001

M3 - Journal article

VL - 287

SP - 42

EP - 53

JO - Mathematical Biosciences

JF - Mathematical Biosciences

SN - 0025-5564

ER -

Research

Associated organisational units

Electronic data

Links

Text available via DOI:

Keywords