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February 2018 Bayesian Nonparametrics for Stochastic Epidemic Models
Theodore Kypraios, Philip D. O’Neill
Statist. Sci. 33(1): 44-56 (February 2018). DOI: 10.1214/17-STS617

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.

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Theodore Kypraios. Philip D. O’Neill. "Bayesian Nonparametrics for Stochastic Epidemic Models." Statist. Sci. 33 (1) 44 - 56, February 2018. https://doi.org/10.1214/17-STS617

Information

Published: February 2018
First available in Project Euclid: 2 February 2018

zbMATH: 07031389
MathSciNet: MR3757503
Digital Object Identifier: 10.1214/17-STS617

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.33 • No. 1 • February 2018
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