Real-time Estimates in Early Detection of SARS

We propose a Bayesian statistical framework for estimating the reproduction number R early in an epidemic. This method allows for the yet-unrecorded secondary cases if the estimate is obtained before the epidemic has ended. We applied our approach to the severe acute respiratory syndrome (SARS) epid...

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Autores principales: Cauchemez, Simon, Boëlle, Pierre-Yves, Donnelly, Christl A., Ferguson, Neil M, Thomas, Guy, Leung, Gabriel M., Hedley, Anthony J, Anderson, Roy M., Valleron, Alain-Jacques
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Centers for Disease Control and Prevention 2006
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3293464/
https://www.ncbi.nlm.nih.gov/pubmed/16494726
http://dx.doi.org/10.3201/eid1201.050593
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author Cauchemez, Simon
Boëlle, Pierre-Yves
Donnelly, Christl A.
Ferguson, Neil M
Thomas, Guy
Leung, Gabriel M.
Hedley, Anthony J
Anderson, Roy M.
Valleron, Alain-Jacques
author_facet Cauchemez, Simon
Boëlle, Pierre-Yves
Donnelly, Christl A.
Ferguson, Neil M
Thomas, Guy
Leung, Gabriel M.
Hedley, Anthony J
Anderson, Roy M.
Valleron, Alain-Jacques
author_sort Cauchemez, Simon
collection PubMed
description We propose a Bayesian statistical framework for estimating the reproduction number R early in an epidemic. This method allows for the yet-unrecorded secondary cases if the estimate is obtained before the epidemic has ended. We applied our approach to the severe acute respiratory syndrome (SARS) epidemic that started in February 2003 in Hong Kong. Temporal patterns of R estimated after 5, 10, and 20 days were similar. Ninety-five percent credible intervals narrowed when more data were available but stabilized after 10 days. Using simulation studies of SARS-like outbreaks, we have shown that the method may be used for early monitoring of the effect of control measures.
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spelling pubmed-32934642012-03-07 Real-time Estimates in Early Detection of SARS Cauchemez, Simon Boëlle, Pierre-Yves Donnelly, Christl A. Ferguson, Neil M Thomas, Guy Leung, Gabriel M. Hedley, Anthony J Anderson, Roy M. Valleron, Alain-Jacques Emerg Infect Dis Research We propose a Bayesian statistical framework for estimating the reproduction number R early in an epidemic. This method allows for the yet-unrecorded secondary cases if the estimate is obtained before the epidemic has ended. We applied our approach to the severe acute respiratory syndrome (SARS) epidemic that started in February 2003 in Hong Kong. Temporal patterns of R estimated after 5, 10, and 20 days were similar. Ninety-five percent credible intervals narrowed when more data were available but stabilized after 10 days. Using simulation studies of SARS-like outbreaks, we have shown that the method may be used for early monitoring of the effect of control measures. Centers for Disease Control and Prevention 2006-01 /pmc/articles/PMC3293464/ /pubmed/16494726 http://dx.doi.org/10.3201/eid1201.050593 Text en https://creativecommons.org/licenses/by/4.0/This is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited.
spellingShingle Research
Cauchemez, Simon
Boëlle, Pierre-Yves
Donnelly, Christl A.
Ferguson, Neil M
Thomas, Guy
Leung, Gabriel M.
Hedley, Anthony J
Anderson, Roy M.
Valleron, Alain-Jacques
Real-time Estimates in Early Detection of SARS
title Real-time Estimates in Early Detection of SARS
title_full Real-time Estimates in Early Detection of SARS
title_fullStr Real-time Estimates in Early Detection of SARS
title_full_unstemmed Real-time Estimates in Early Detection of SARS
title_short Real-time Estimates in Early Detection of SARS
title_sort real-time estimates in early detection of sars
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3293464/
https://www.ncbi.nlm.nih.gov/pubmed/16494726
http://dx.doi.org/10.3201/eid1201.050593
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