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Bayesian nonparametrics for stochastic epidemic models

Kypraios, Theodore; O'Neill, Philip D.

Authors

PHILIP O'NEILL philip.oneill@nottingham.ac.uk
Professor of Applied Probability



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.

Journal Article Type Article
Publication 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
APA6 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
DOI https://doi.org/10.1214/17-STS617
Keywords Bayesian nonparametrics, Epidemic model, Gaussian process
Publisher URL https://projecteuclid.org/euclid.ss/1517562024
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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