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, R code to implement the algorithms presented in the paper is available on https://github.com/kypraios/epiABC.
authors.affiliation: School of Mathematical Sciences, University of Nottingham, UK. Electronic address: theodore.kypraios@nottingham.ac.uk.
authors.forename: Theodore
authors.initials: T
authors.lastname: Kypraios
authors.affiliation: Department of Mathematics and Statistics, Lancaster University, UK.
authors.forename: Peter
authors.initials: P
authors.lastname: Neal
authors.affiliation: School of Mathematics and Statistics, Newcastle University, UK.
authors.forename: Dennis
authors.initials: D
authors.lastname: Prangle
mesh_terms: D001499:Bayes Theorem; D058872:Epidemics; D006801:Humans; D008962:Models, Theoretical; D013269:Stochastic Processes
keywords: Approximate Bayesian Computation; Bayesian inference; Epidemics; Population Monte Carlo; Stochastic epidemic models