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

Kypraios, Theodore and Neal, Peter and Prangle, Dennis (2017) A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation. Mathematical Biosciences, 287 . pp. 42-53. ISSN 1879-3134

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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.

Item Type: Article
Keywords: Bayesian inference; Epidemics; Stochastic epidemic models; Approximate Bayesian Computation; Population Monte Carlo
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
Identification Number: 10.1016/j.mbs.2016.07.001
Depositing User: Eprints, Support
Date Deposited: 06 Jul 2017 09:45
Last Modified: 19 Jul 2017 15:18
URI: http://eprints.nottingham.ac.uk/id/eprint/44015

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