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Multiple Estimates of Transmissibility for the 2009 Influenza Pandemic Based on Influenza-like-Illness Data from Small US Military Populations

Rapidly characterizing the amplitude and variability in transmissibility of novel human influenza strains as they emerge is a key public health priority. However, comparison of early estimates of the basic reproduction number during the 2009 pandemic were challenging because of inconsistent data sou...

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Autores principales: Riley, Pete, Ben-Nun, Michal, Armenta, Richard, Linker, Jon A., Eick, Angela A., Sanchez, Jose L., George, Dylan, Bacon, David P., Riley, Steven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3656103/
https://www.ncbi.nlm.nih.gov/pubmed/23696723
http://dx.doi.org/10.1371/journal.pcbi.1003064
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author Riley, Pete
Ben-Nun, Michal
Armenta, Richard
Linker, Jon A.
Eick, Angela A.
Sanchez, Jose L.
George, Dylan
Bacon, David P.
Riley, Steven
author_facet Riley, Pete
Ben-Nun, Michal
Armenta, Richard
Linker, Jon A.
Eick, Angela A.
Sanchez, Jose L.
George, Dylan
Bacon, David P.
Riley, Steven
author_sort Riley, Pete
collection PubMed
description Rapidly characterizing the amplitude and variability in transmissibility of novel human influenza strains as they emerge is a key public health priority. However, comparison of early estimates of the basic reproduction number during the 2009 pandemic were challenging because of inconsistent data sources and methods. Here, we define and analyze influenza-like-illness (ILI) case data from 2009–2010 for the 50 largest spatially distinct US military installations (military population defined by zip code, MPZ). We used publicly available data from non-military sources to show that patterns of ILI incidence in many of these MPZs closely followed the pattern of their enclosing civilian population. After characterizing the broad patterns of incidence (e.g. single-peak, double-peak), we defined a parsimonious SIR-like model with two possible values for intrinsic transmissibility across three epochs. We fitted the parameters of this model to data from all 50 MPZs, finding them to be reasonably well clustered with a median (mean) value of 1.39 (1.57) and standard deviation of 0.41. An increasing temporal trend in transmissibility ([Image: see text], p-value: 0.013) during the period of our study was robust to the removal of high transmissibility outliers and to the removal of the smaller 20 MPZs. Our results demonstrate the utility of rapidly available – and consistent – data from multiple populations.
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spelling pubmed-36561032013-05-21 Multiple Estimates of Transmissibility for the 2009 Influenza Pandemic Based on Influenza-like-Illness Data from Small US Military Populations Riley, Pete Ben-Nun, Michal Armenta, Richard Linker, Jon A. Eick, Angela A. Sanchez, Jose L. George, Dylan Bacon, David P. Riley, Steven PLoS Comput Biol Research Article Rapidly characterizing the amplitude and variability in transmissibility of novel human influenza strains as they emerge is a key public health priority. However, comparison of early estimates of the basic reproduction number during the 2009 pandemic were challenging because of inconsistent data sources and methods. Here, we define and analyze influenza-like-illness (ILI) case data from 2009–2010 for the 50 largest spatially distinct US military installations (military population defined by zip code, MPZ). We used publicly available data from non-military sources to show that patterns of ILI incidence in many of these MPZs closely followed the pattern of their enclosing civilian population. After characterizing the broad patterns of incidence (e.g. single-peak, double-peak), we defined a parsimonious SIR-like model with two possible values for intrinsic transmissibility across three epochs. We fitted the parameters of this model to data from all 50 MPZs, finding them to be reasonably well clustered with a median (mean) value of 1.39 (1.57) and standard deviation of 0.41. An increasing temporal trend in transmissibility ([Image: see text], p-value: 0.013) during the period of our study was robust to the removal of high transmissibility outliers and to the removal of the smaller 20 MPZs. Our results demonstrate the utility of rapidly available – and consistent – data from multiple populations. Public Library of Science 2013-05-16 /pmc/articles/PMC3656103/ /pubmed/23696723 http://dx.doi.org/10.1371/journal.pcbi.1003064 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
spellingShingle Research Article
Riley, Pete
Ben-Nun, Michal
Armenta, Richard
Linker, Jon A.
Eick, Angela A.
Sanchez, Jose L.
George, Dylan
Bacon, David P.
Riley, Steven
Multiple Estimates of Transmissibility for the 2009 Influenza Pandemic Based on Influenza-like-Illness Data from Small US Military Populations
title Multiple Estimates of Transmissibility for the 2009 Influenza Pandemic Based on Influenza-like-Illness Data from Small US Military Populations
title_full Multiple Estimates of Transmissibility for the 2009 Influenza Pandemic Based on Influenza-like-Illness Data from Small US Military Populations
title_fullStr Multiple Estimates of Transmissibility for the 2009 Influenza Pandemic Based on Influenza-like-Illness Data from Small US Military Populations
title_full_unstemmed Multiple Estimates of Transmissibility for the 2009 Influenza Pandemic Based on Influenza-like-Illness Data from Small US Military Populations
title_short Multiple Estimates of Transmissibility for the 2009 Influenza Pandemic Based on Influenza-like-Illness Data from Small US Military Populations
title_sort multiple estimates of transmissibility for the 2009 influenza pandemic based on influenza-like-illness data from small us military populations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3656103/
https://www.ncbi.nlm.nih.gov/pubmed/23696723
http://dx.doi.org/10.1371/journal.pcbi.1003064
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