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Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes

This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these...

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Detalles Bibliográficos
Autores principales: Xu, Xiaoguang, Kypraios, Theodore, O'Neill, Philip D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5031942/
https://www.ncbi.nlm.nih.gov/pubmed/26993062
http://dx.doi.org/10.1093/biostatistics/kxw011
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author Xu, Xiaoguang
Kypraios, Theodore
O'Neill, Philip D
author_facet Xu, Xiaoguang
Kypraios, Theodore
O'Neill, Philip D
author_sort Xu, Xiaoguang
collection PubMed
description This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings.
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spelling pubmed-50319422016-09-23 Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes Xu, Xiaoguang Kypraios, Theodore O'Neill, Philip D Biostatistics Articles This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings. Oxford University Press 2016-10 2016-03-18 /pmc/articles/PMC5031942/ /pubmed/26993062 http://dx.doi.org/10.1093/biostatistics/kxw011 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Xu, Xiaoguang
Kypraios, Theodore
O'Neill, Philip D
Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes
title Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes
title_full Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes
title_fullStr Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes
title_full_unstemmed Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes
title_short Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes
title_sort bayesian non-parametric inference for stochastic epidemic models using gaussian processes
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5031942/
https://www.ncbi.nlm.nih.gov/pubmed/26993062
http://dx.doi.org/10.1093/biostatistics/kxw011
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