Bayesian nonparametrics for stochastic epidemic models

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. Speci...

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Bibliographic Details
Main Authors: Kypraios, Theodore, O'Neill, Philip D.
Format: Article
Published: Institute of Mathematical Statistics 2018
Subjects:
Online Access:https://eprints.nottingham.ac.uk/43486/
Description
Summary: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.