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FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations

Overview of attention for article published in BMC Public Health, October 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#49 of 14,923)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
59 news outlets
blogs
2 blogs
twitter
15 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
151 Dimensions

Readers on

mendeley
221 Mendeley
citeulike
1 CiteULike
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Title
FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations
Published in
BMC Public Health, October 2013
DOI 10.1186/1471-2458-13-940
Pubmed ID
Authors

John J Grefenstette, Shawn T Brown, Roni Rosenfeld, Jay DePasse, Nathan TB Stone, Phillip C Cooley, William D Wheaton, Alona Fyshe, David D Galloway, Anuroop Sriram, Hasan Guclu, Thomas Abraham, Donald S Burke

Abstract

Mathematical and computational models provide valuable tools that help public health planners to evaluate competing health interventions, especially for novel circumstances that cannot be examined through observational or controlled studies, such as pandemic influenza. The spread of diseases like influenza depends on the mixing patterns within the population, and these mixing patterns depend in part on local factors including the spatial distribution and age structure of the population, the distribution of size and composition of households, employment status and commuting patterns of adults, and the size and age structure of schools. Finally, public health planners must take into account the health behavior patterns of the population, patterns that often vary according to socioeconomic factors such as race, household income, and education levels.

Twitter Demographics

The data shown below were collected from the profiles of 15 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 221 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 4%
Israel 1 <1%
United Kingdom 1 <1%
Belgium 1 <1%
Canada 1 <1%
China 1 <1%
Denmark 1 <1%
Unknown 206 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 46 21%
Student > Ph. D. Student 32 14%
Student > Master 24 11%
Professor > Associate Professor 16 7%
Professor 15 7%
Other 56 25%
Unknown 32 14%
Readers by discipline Count As %
Computer Science 34 15%
Medicine and Dentistry 29 13%
Social Sciences 15 7%
Agricultural and Biological Sciences 14 6%
Mathematics 11 5%
Other 67 30%
Unknown 51 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 465. 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 09 January 2023.
All research outputs
#47,632
of 22,889,074 outputs
Outputs from BMC Public Health
#49
of 14,923 outputs
Outputs of similar age
#292
of 209,724 outputs
Outputs of similar age from BMC Public Health
#1
of 283 outputs
Altmetric has tracked 22,889,074 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,923 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one has done particularly well, scoring higher than 99% 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 209,724 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 99% of its contemporaries.
We're also able to compare this research output to 283 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 99% of its contemporaries.