Statistical Science

Bayesian Nonparametrics for Stochastic Epidemic Models

Theodore Kypraios and Philip D. O’Neill

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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.

Article information

Source
Statist. Sci., Volume 33, Number 1 (2018), 44-56.

Dates
First available in Project Euclid: 2 February 2018

Permanent link to this document
https://projecteuclid.org/euclid.ss/1517562024

Digital Object Identifier
doi:10.1214/17-STS617

Mathematical Reviews number (MathSciNet)
MR3757503

Zentralblatt MATH identifier
07031389

Keywords
Bayesian nonparametrics epidemic model Gaussian process

Citation

Kypraios, Theodore; O’Neill, Philip D. Bayesian Nonparametrics for Stochastic Epidemic Models. Statist. Sci. 33 (2018), no. 1, 44--56. doi:10.1214/17-STS617. https://projecteuclid.org/euclid.ss/1517562024


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