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Spatial Transmission of 2009 Pandemic Influenza in the US

Overview of attention for article published in PLoS Computational Biology, June 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
12 news outlets
blogs
5 blogs
policy
1 policy source
twitter
12 X users
wikipedia
3 Wikipedia pages
googleplus
1 Google+ user

Citations

dimensions_citation
151 Dimensions

Readers on

mendeley
201 Mendeley
citeulike
1 CiteULike
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Title
Spatial Transmission of 2009 Pandemic Influenza in the US
Published in
PLoS Computational Biology, June 2014
DOI 10.1371/journal.pcbi.1003635
Pubmed ID
Authors

Julia R. Gog, Sébastien Ballesteros, Cécile Viboud, Lone Simonsen, Ottar N. Bjornstad, Jeffrey Shaman, Dennis L. Chao, Farid Khan, Bryan T. Grenfell

Abstract

The 2009 H1N1 influenza pandemic provides a unique opportunity for detailed examination of the spatial dynamics of an emerging pathogen. In the US, the pandemic was characterized by substantial geographical heterogeneity: the 2009 spring wave was limited mainly to northeastern cities while the larger fall wave affected the whole country. Here we use finely resolved spatial and temporal influenza disease data based on electronic medical claims to explore the spread of the fall pandemic wave across 271 US cities and associated suburban areas. We document a clear spatial pattern in the timing of onset of the fall wave, starting in southeastern cities and spreading outwards over a period of three months. We use mechanistic models to tease apart the external factors associated with the timing of the fall wave arrival: differential seeding events linked to demographic factors, school opening dates, absolute humidity, prior immunity from the spring wave, spatial diffusion, and their interactions. Although the onset of the fall wave was correlated with school openings as previously reported, models including spatial spread alone resulted in better fit. The best model had a combination of the two. Absolute humidity or prior exposure during the spring wave did not improve the fit and population size only played a weak role. In conclusion, the protracted spread of pandemic influenza in fall 2009 in the US was dominated by short-distance spatial spread partially catalysed by school openings rather than long-distance transmission events. This is in contrast to the rapid hierarchical transmission patterns previously described for seasonal influenza. The findings underline the critical role that school-age children play in facilitating the geographic spread of pandemic influenza and highlight the need for further information on the movement and mixing patterns of this age group.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 201 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 3%
United Kingdom 3 1%
Brazil 2 <1%
Kenya 1 <1%
Australia 1 <1%
Vietnam 1 <1%
Italy 1 <1%
Portugal 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 183 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 45 22%
Student > Ph. D. Student 41 20%
Student > Master 24 12%
Student > Bachelor 16 8%
Student > Postgraduate 9 4%
Other 38 19%
Unknown 28 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 15%
Medicine and Dentistry 28 14%
Mathematics 25 12%
Social Sciences 10 5%
Computer Science 8 4%
Other 56 28%
Unknown 44 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 128. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 October 2021.
All research outputs
#327,198
of 25,461,852 outputs
Outputs from PLoS Computational Biology
#224
of 8,981 outputs
Outputs of similar age
#2,696
of 243,685 outputs
Outputs of similar age from PLoS Computational Biology
#3
of 142 outputs
Altmetric has tracked 25,461,852 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,981 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 243,685 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 142 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.