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Detecting signals of seasonal influenza severity through age dynamics

Overview of attention for article published in BMC Infectious Diseases, December 2015
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
1 news outlet
twitter
12 tweeters
facebook
3 Facebook pages

Citations

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12 Dimensions

Readers on

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69 Mendeley
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Title
Detecting signals of seasonal influenza severity through age dynamics
Published in
BMC Infectious Diseases, December 2015
DOI 10.1186/s12879-015-1318-9
Pubmed ID
Authors

Elizabeth C. Lee, Cécile Viboud, Lone Simonsen, Farid Khan, Shweta Bansal

Abstract

Measures of population-level influenza severity are important for public health planning, but estimates are often based on case-fatality and case-hospitalization risks, which require multiple data sources, are prone to surveillance biases, and are typically unavailable in the early stages of an outbreak. To address the limitations of traditional indicators, we propose a novel severity index based on influenza age dynamics estimated from routine physician diagnosis data that can be used retrospectively and for early warning. We developed a quantitative 'ground truth' severity benchmark that synthesizes multiple traditional severity indicators from publicly available influenza surveillance data in the United States. Observing that the age distribution of cases may signal severity early in an epidemic, we constructed novel retrospective and early warning severity indexes based on the relative risk of influenza-like illness (ILI) among working-age adults to that among school-aged children using weekly outpatient medical claims. We compared our relative risk-based indexes to the composite benchmark and estimated seasonal severity for flu seasons from 2001-02 to 2008-09 at the national and state levels. The severity classifications made by the benchmark were not uniquely captured by any single contributing metric, including pneumonia and influenza mortality; the influenza epidemics of 2003-04 and 2007-08 were correctly identified as the most severe of the study period. The retrospective index was well correlated with the severity benchmark and correctly identified the two most severe seasons. The early warning index performance varied, but it projected 2007-08 as relatively severe 10 weeks prior to the epidemic peak. Influenza severity varied significantly among states within seasons, and four states were identified as possible early warning sentinels for national severity. Differences in age patterns of ILI may be used to characterize seasonal influenza severity in the United States in real-time and in a spatially resolved way. Future research on antigenic changes among circulating viruses, pre-existing immunity, and changing contact patterns may better elucidate the mechanisms underlying these indexes. Researchers and practitioners should consider the use of composite or ILI-based severity metrics in addition to traditional severity measures to inform epidemiological understanding and situational awareness in future seasonal outbreaks.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Vietnam 1 1%
Unknown 67 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 19%
Researcher 11 16%
Student > Doctoral Student 6 9%
Student > Master 6 9%
Student > Bachelor 5 7%
Other 15 22%
Unknown 13 19%
Readers by discipline Count As %
Medicine and Dentistry 17 25%
Agricultural and Biological Sciences 11 16%
Mathematics 7 10%
Computer Science 2 3%
Nursing and Health Professions 2 3%
Other 11 16%
Unknown 19 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 01 November 2016.
All research outputs
#1,665,682
of 21,377,679 outputs
Outputs from BMC Infectious Diseases
#428
of 7,295 outputs
Outputs of similar age
#35,064
of 404,297 outputs
Outputs of similar age from BMC Infectious Diseases
#32
of 585 outputs
Altmetric has tracked 21,377,679 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,295 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has done particularly well, scoring higher than 94% 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 404,297 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 91% of its contemporaries.
We're also able to compare this research output to 585 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 94% of its contemporaries.