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Determinants of the Spatiotemporal Dynamics of the 2009 H1N1 Pandemic in Europe: Implications for Real-Time Modelling

Overview of attention for article published in PLoS Computational Biology, September 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

policy
3 policy sources
facebook
1 Facebook page

Citations

dimensions_citation
111 Dimensions

Readers on

mendeley
148 Mendeley
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2 CiteULike
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Title
Determinants of the Spatiotemporal Dynamics of the 2009 H1N1 Pandemic in Europe: Implications for Real-Time Modelling
Published in
PLoS Computational Biology, September 2011
DOI 10.1371/journal.pcbi.1002205
Pubmed ID
Authors

Stefano Merler, Marco Ajelli, Andrea Pugliese, Neil M. Ferguson

Abstract

Influenza pandemics in the last century were characterized by successive waves and differences in impact and timing between different regions, for reasons not clearly understood. The 2009 H1N1 pandemic showed rapid global spread, but with substantial heterogeneity in timing within each hemisphere. Even within Europe substantial variation was observed, with the UK being unique in experiencing a major first wave of transmission in early summer and all other countries having a single major epidemic in the autumn/winter, with a West to East pattern of spread. Here we show that a microsimulation model, parameterised using data about H1N1pdm collected by the beginning of June 2009, explains the occurrence of two waves in UK and a single wave in the rest of Europe as a consequence of timing of H1N1pdm spread, fluxes of travels from US and Mexico, and timing of school vacations. The model provides a description of pandemic spread through Europe, depending on intra-European mobility patterns and socio-demographic structure of the European populations, which is in broad agreement with observed timing of the pandemic in different countries. Attack rates are predicted to depend on the socio-demographic structure, with age dependent attack rates broadly agreeing with available serological data. Results suggest that the observed heterogeneity can be partly explained by the between country differences in Europe: marked differences in school calendars, mobility patterns and sociodemographic structures. Moreover, higher susceptibility of children to infection played a key role in determining the epidemiology of the 2009 pandemic. Our work shows that it would have been possible to obtain a broad-brush prediction of timing of the European pandemic well before the autumn of 2009, much more difficult to achieve with simpler models or pre-pandemic parameterisation. This supports the use of models accounting for the structure of complex modern societies for giving insight to policy makers.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 3 2%
United Kingdom 3 2%
United States 2 1%
Sweden 1 <1%
Israel 1 <1%
Australia 1 <1%
Mexico 1 <1%
Vietnam 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 133 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 40 27%
Researcher 34 23%
Student > Master 12 8%
Professor 10 7%
Student > Doctoral Student 7 5%
Other 34 23%
Unknown 11 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 16%
Medicine and Dentistry 22 15%
Physics and Astronomy 14 9%
Mathematics 13 9%
Computer Science 12 8%
Other 39 26%
Unknown 25 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 13 November 2023.
All research outputs
#3,713,898
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#3,226
of 8,964 outputs
Outputs of similar age
#19,494
of 143,376 outputs
Outputs of similar age from PLoS Computational Biology
#32
of 121 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 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 gotten more attention than average, scoring higher than 63% 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 143,376 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 121 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.