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Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data

Whole‐genome sequencing of pathogens in outbreaks of infectious disease provides the potential to reconstruct transmission pathways and enhance the information contained in conventional epidemiological data. In recent years, there have been numerous new methods and models developed to exploit such h...

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Detalles Bibliográficos
Autores principales: Cassidy, Rosanna, Kypraios, Theodore, O'Neill, Philip D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217057/
https://www.ncbi.nlm.nih.gov/pubmed/32142587
http://dx.doi.org/10.1002/sim.8510
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author Cassidy, Rosanna
Kypraios, Theodore
O'Neill, Philip D.
author_facet Cassidy, Rosanna
Kypraios, Theodore
O'Neill, Philip D.
author_sort Cassidy, Rosanna
collection PubMed
description Whole‐genome sequencing of pathogens in outbreaks of infectious disease provides the potential to reconstruct transmission pathways and enhance the information contained in conventional epidemiological data. In recent years, there have been numerous new methods and models developed to exploit such high‐resolution genetic data. However, corresponding methods for model assessment have been largely overlooked. In this article, we develop both new modelling methods and new model assessment methods, specifically by building on the work of Worby et al. Although the methods are generic in nature, we focus specifically on nosocomial pathogens and analyze a dataset collected during an outbreak of MRSA in a hospital setting.
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spelling pubmed-72170572020-05-13 Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data Cassidy, Rosanna Kypraios, Theodore O'Neill, Philip D. Stat Med Research Articles Whole‐genome sequencing of pathogens in outbreaks of infectious disease provides the potential to reconstruct transmission pathways and enhance the information contained in conventional epidemiological data. In recent years, there have been numerous new methods and models developed to exploit such high‐resolution genetic data. However, corresponding methods for model assessment have been largely overlooked. In this article, we develop both new modelling methods and new model assessment methods, specifically by building on the work of Worby et al. Although the methods are generic in nature, we focus specifically on nosocomial pathogens and analyze a dataset collected during an outbreak of MRSA in a hospital setting. John Wiley and Sons Inc. 2020-03-06 2020-05-30 /pmc/articles/PMC7217057/ /pubmed/32142587 http://dx.doi.org/10.1002/sim.8510 Text en © 2020 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Cassidy, Rosanna
Kypraios, Theodore
O'Neill, Philip D.
Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data
title Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data
title_full Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data
title_fullStr Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data
title_full_unstemmed Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data
title_short Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data
title_sort modelling, bayesian inference, and model assessment for nosocomial pathogens using whole‐genome‐sequence data
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217057/
https://www.ncbi.nlm.nih.gov/pubmed/32142587
http://dx.doi.org/10.1002/sim.8510
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