Year: 2017
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Kypraios, Theodore, O'Neill, Philip D.
Institution: University of Nottingham
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2963894969