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Enhancing disease surveillance with novel data streams: challenges and opportunities

Overview of attention for article published in EPJ Data Science, October 2015
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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 (#17 of 108)
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

news
4 news outlets
twitter
42 tweeters
facebook
3 Facebook pages
googleplus
1 Google+ user

Readers on

mendeley
18 Mendeley
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Title
Enhancing disease surveillance with novel data streams: challenges and opportunities
Published in
EPJ Data Science, October 2015
DOI 10.1140/epjds/s13688-015-0054-0
Pubmed ID
Authors

Benjamin M Althouse, Samuel V Scarpino, Lauren A Meyers, John W Ayers, Marisa Bargsten, Joan Baumbach, John S Brownstein, Lauren Castro, Hannah Clapham, Derek AT Cummings, Sara Del Valle, Stephen Eubank, Geoffrey Fairchild, Lyn Finelli, Nicholas Generous, Dylan George, David R Harper, Laurent Hébert-Dufresne, Michael A Johansson, Kevin Konty, Marc Lipsitch, Gabriel Milinovich, Joseph D Miller, Elaine O Nsoesie, Donald R Olson, Michael Paul, Philip M Polgreen, Reid Priedhorsky, Jonathan M Read, Isabel Rodríguez-Barraquer, Derek J Smith, Christian Stefansen, David L Swerdlow, Deborah Thompson, Alessandro Vespignani, Amy Wesolowski, Lauren Ancel Meyers, Althouse, Benjamin M, Scarpino, Samuel V, Meyers, Lauren Ancel, Ayers, John W, Bargsten, Marisa, Baumbach, Joan, Brownstein, John S, Castro, Lauren, Clapham, Hannah, Cummings, Derek At, Del Valle, Sara, Eubank, Stephen, Fairchild, Geoffrey, Finelli, Lyn, Generous, Nicholas, George, Dylan, Harper, David R, Hébert-Dufresne, Laurent, Johansson, Michael A, Konty, Kevin, Lipsitch, Marc, Milinovich, Gabriel, Miller, Joseph D, Nsoesie, Elaine O, Olson, Donald R, Paul, Michael, Polgreen, Philip M, Priedhorsky, Reid, Read, Jonathan M, Rodríguez-Barraquer, Isabel, Smith, Derek J, Stefansen, Christian, Swerdlow, David L, Thompson, Deborah, Vespignani, Alessandro, Wesolowski, Amy

Abstract

Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.

Twitter Demographics

The data shown below were collected from the profiles of 42 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 6%
Unknown 17 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 33%
Student > Doctoral Student 4 22%
Student > Ph. D. Student 3 17%
Student > Master 2 11%
Professor 1 6%
Other 2 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 22%
Mathematics 3 17%
Medicine and Dentistry 3 17%
Computer Science 2 11%
Arts and Humanities 1 6%
Other 5 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 63. 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 18 March 2016.
All research outputs
#128,589
of 7,822,140 outputs
Outputs from EPJ Data Science
#17
of 108 outputs
Outputs of similar age
#7,466
of 242,229 outputs
Outputs of similar age from EPJ Data Science
#1
of 10 outputs
Altmetric has tracked 7,822,140 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 108 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 38.8. This one has done well, scoring higher than 84% 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 242,229 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 96% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them