Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes

Xu, Xiaoguang; Kypraios, Theodore; O'Neill, Philip D.

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.

Citation

Xu, X., Kypraios, T., & O'Neill, P. D. (2016). Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes. Biostatistics, 17(4), 619-633. https://doi.org/10.1093/biostatistics/kxw011

• Journal Article Type: Article
• Acceptance Date: Feb 7, 2016
• Publication Date: Mar 8, 2016
• Deposit Date: Mar 15, 2018
• Publicly Available Date: Jan 21, 2019
• Print ISSN: 1465-4644
• Electronic ISSN: 1468-4357
• Publisher: Oxford University Press
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 4
• Pages: 619-633
• DOI: https://doi.org/10.1093/biostatistics/kxw011
• Public URL: https://nottingham-repository.worktribe.com/output/1109001
• Publisher URL: https://academic.oup.com/biostatistics/article/17/4/619/2800187#124412192