A Bayesian approach for detecting a disease that is not being modeled

Over the past decade, outbreaks of new or reemergent viruses such as severe acute respiratory syndrome (SARS) virus, Middle East respiratory syndrome (MERS) virus, and Zika have claimed thousands of lives and cost governments and healthcare systems billions of dollars. Because the appearance of new...

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Detalles Bibliográficos
Autores principales: Aronis, John M., Ferraro, Jeffrey P., Gesteland, Per H., Tsui, Fuchiang, Ye, Ye, Wagner, Michael M., Cooper, Gregory F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7048291/
https://www.ncbi.nlm.nih.gov/pubmed/32109254
http://dx.doi.org/10.1371/journal.pone.0229658
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author Aronis, John M.
Ferraro, Jeffrey P.
Gesteland, Per H.
Tsui, Fuchiang
Ye, Ye
Wagner, Michael M.
Cooper, Gregory F.
author_facet Aronis, John M.
Ferraro, Jeffrey P.
Gesteland, Per H.
Tsui, Fuchiang
Ye, Ye
Wagner, Michael M.
Cooper, Gregory F.
author_sort Aronis, John M.
collection PubMed
description Over the past decade, outbreaks of new or reemergent viruses such as severe acute respiratory syndrome (SARS) virus, Middle East respiratory syndrome (MERS) virus, and Zika have claimed thousands of lives and cost governments and healthcare systems billions of dollars. Because the appearance of new or transformed diseases is likely to continue, the detection and characterization of emergent diseases is an important problem. We describe a Bayesian statistical model that can detect and characterize previously unknown and unmodeled diseases from patient-care reports and evaluate its performance on historical data.
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spelling pubmed-70482912020-03-09 A Bayesian approach for detecting a disease that is not being modeled Aronis, John M. Ferraro, Jeffrey P. Gesteland, Per H. Tsui, Fuchiang Ye, Ye Wagner, Michael M. Cooper, Gregory F. PLoS One Research Article Over the past decade, outbreaks of new or reemergent viruses such as severe acute respiratory syndrome (SARS) virus, Middle East respiratory syndrome (MERS) virus, and Zika have claimed thousands of lives and cost governments and healthcare systems billions of dollars. Because the appearance of new or transformed diseases is likely to continue, the detection and characterization of emergent diseases is an important problem. We describe a Bayesian statistical model that can detect and characterize previously unknown and unmodeled diseases from patient-care reports and evaluate its performance on historical data. Public Library of Science 2020-02-28 /pmc/articles/PMC7048291/ /pubmed/32109254 http://dx.doi.org/10.1371/journal.pone.0229658 Text en © 2020 Aronis et al 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 use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Aronis, John M.
Ferraro, Jeffrey P.
Gesteland, Per H.
Tsui, Fuchiang
Ye, Ye
Wagner, Michael M.
Cooper, Gregory F.
A Bayesian approach for detecting a disease that is not being modeled
title A Bayesian approach for detecting a disease that is not being modeled
title_full A Bayesian approach for detecting a disease that is not being modeled
title_fullStr A Bayesian approach for detecting a disease that is not being modeled
title_full_unstemmed A Bayesian approach for detecting a disease that is not being modeled
title_short A Bayesian approach for detecting a disease that is not being modeled
title_sort bayesian approach for detecting a disease that is not being modeled
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7048291/
https://www.ncbi.nlm.nih.gov/pubmed/32109254
http://dx.doi.org/10.1371/journal.pone.0229658
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