title: Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.
abstract: This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings.
authors.affiliation: Institute of Population Health, University of Manchester, Manchester, UK.
authors.forename: Xiaoguang
authors.initials: X
authors.lastname: Xu
authors.affiliation: School of Mathematical Sciences, University of Nottingham, Nottingham, UK.
authors.forename: Theodore
authors.initials: T
authors.lastname: Kypraios
authors.affiliation: School of Mathematical Sciences, University of Nottingham, Nottingham, UK philip.oneill@nottingham.ac.uk.
authors.forename: Philip D
authors.initials: PD
authors.lastname: O'Neill
mesh_terms: D001499:Bayes Theorem; D058872:Epidemics; D006801:Humans; D008962:Models, Theoretical; D016011:Normal Distribution; D013269:Stochastic Processes
keywords: Bayesian non-parametrics; Epidemic model; Gaussian process; SIR model