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    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

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

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<mark>Journal publication date</mark>05/2017
<mark>Journal</mark>Mathematical Biosciences
Volume287
Number of pages12
Pages (from-to)42-53
Publication StatusPublished
Early online date18/07/16
<mark>Original language</mark>English

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.

Bibliographic note

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