Bayesian nonparametrics for stochastic epidemic models

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Kypraios, Theodore and O'Neill, Philip D. (2018) Bayesian nonparametrics for stochastic epidemic models. Statistical Science, 33 (1). pp. 44-56. ISSN 2168-8745

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/908545
Keywords: Bayesian nonparametrics, Epidemic model, Gaussian process
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
Identification Number: https://doi.org/10.1214/17-STS617
Depositing User: O'neill, Philip
Date Deposited: 09 Jun 2017 10:08
Last Modified: 04 May 2020 19:29
URI: https://eprints.nottingham.ac.uk/id/eprint/43486

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