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    Rights statement: NOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics and Data Analysis. 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 Computational Statistics and Data Analysis [80, 2014] DOI: 10.1016/j.csda.2014.07.002

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Efficient MCMC for temporal epidemics via parameter reduction

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>12/2014
<mark>Journal</mark>Computational Statistics and Data Analysis
Volume80
Number of pages11
Pages (from-to)240-250
Publication StatusPublished
Early online date14/07/14
<mark>Original language</mark>English

Abstract

An efficient, generic and simple to use Markov chain Monte Carlo (MCMC) algorithm for partially observed temporal epidemic models is introduced. The algorithm is designed to be adaptive so that it can easily be used by non-experts. There are two key features incorporated in the algorithm to develop an efficient algorithm, parameter reduction and efficient, multiple updates of the augmented infection times. The algorithm is successfully applied to two real life epidemic data sets, the Abakaliki smallpox data and the 2001 UK foot-and-mouth epidemic in Cumbria.

Bibliographic note

NOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics and Data Analysis. 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 Computational Statistics and Data Analysis [80, 2014] DOI: 10.1016/j.csda.2014.07.002