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

Kypraios, Theodore; Neal, Peter; Prangle, Dennis

Authors

Theodore Kypraios theodore.kypraios@nottingham.ac.uk

Peter Neal

Dennis Prangle



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, R code to implement the algorithms presented in the paper is available on https://github.com/kypraios/epiABC.

Journal Article Type Article
Publication Date May 31, 2017
Journal Mathematical Biosciences
Print ISSN 0025-5564
Electronic ISSN 1879-3134
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 287
APA6 Citation Kypraios, T., Neal, P., & Prangle, D. (2017). A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation. Mathematical Biosciences, 287, doi:10.1016/j.mbs.2016.07.001
DOI https://doi.org/10.1016/j.mbs.2016.07.001
Keywords Bayesian inference; Epidemics; Stochastic epidemic models; Approximate Bayesian Computation; Population Monte Carlo
Publisher URL https://doi.org/10.1016/j.mbs.2016.07.001
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0

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Tutorial mainR1.pdf (363 Kb)
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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





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