Bayesian nonparametrics for stochastic epidemic models

Kypraios, Theodore; O'Neill, Philip D.

Abstract

The vast majority of models for the spread of communicable diseases are parametric in nature and involve underlying assumptions about how the disease spreads through a population. In this article we consider the use of Bayesian nonparametric approaches to analysing data from disease outbreaks. Specifically we focus on methods for estimating the infection process in simple models under the assumption that this process has an explicit time-dependence.

Citation

Kypraios, T., & O'Neill, P. D. (2018). Bayesian nonparametrics for stochastic epidemic models. Statistical Science, 33(1), https://doi.org/10.1214/17-STS617

• Journal Article Type: Article
• Acceptance Date: Jun 3, 2017
• Publication Date: Feb 2, 2018
• Deposit Date: Jun 9, 2017
• Publicly Available Date: Feb 2, 2018
• Journal: Statistical Science
• Print ISSN: 0883-4237
• Electronic ISSN: 2168-8745
• Publisher: Institute of Mathematical Statistics (IMS)
• Peer Reviewed: Peer Reviewed
• Volume: 33
• Issue: 1
• DOI: https://doi.org/10.1214/17-STS617
• Keywords: Bayesian nonparametrics, Epidemic model, Gaussian process
• Public URL: https://nottingham-repository.worktribe.com/output/908545
• Publisher URL: https://projecteuclid.org/euclid.ss/1517562024
• Contract Date: Jun 9, 2017