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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doaj-411cedfeb38e4b4d82912623dd7cc3ab2021-04-21T15:09:24ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582013-01-0195e100306410.1371/journal.pcbi.1003064Multiple estimates of transmissibility for the 2009 influenza pandemic based on influenza-like-illness data from small US military populations.Pete RileyMichal Ben-NunRichard ArmentaJon A LinkerAngela A EickJose L SanchezDylan GeorgeDavid P BaconSteven RileyRapidly 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 ([Formula: 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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23696723/pdf/?tool=EBI |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pete Riley Michal Ben-Nun Richard Armenta Jon A Linker Angela A Eick Jose L Sanchez Dylan George David P Bacon Steven Riley |
spellingShingle |
Pete Riley Michal Ben-Nun Richard Armenta Jon A Linker Angela A Eick Jose L Sanchez Dylan George David P Bacon Steven Riley Multiple estimates of transmissibility for the 2009 influenza pandemic based on influenza-like-illness data from small US military populations. PLoS Computational Biology |
author_facet |
Pete Riley Michal Ben-Nun Richard Armenta Jon A Linker Angela A Eick Jose L Sanchez Dylan George David P Bacon Steven Riley |
author_sort |
Pete Riley |
title |
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_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_sort |
multiple estimates of transmissibility for the 2009 influenza pandemic based on influenza-like-illness data from small us military populations. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Computational Biology |
issn |
1553-734X 1553-7358 |
publishDate |
2013-01-01 |
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 ([Formula: 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. |
url |
https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23696723/pdf/?tool=EBI |
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