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Real-time tracking of self-reported symptoms to predict potential COVID-19

Overview of attention for article published in Nature Medicine, May 2020
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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 (#13 of 7,248)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
100 news outlets
blogs
16 blogs
policy
3 policy sources
twitter
3898 tweeters
facebook
5 Facebook pages
wikipedia
2 Wikipedia pages
reddit
3 Redditors

Citations

dimensions_citation
154 Dimensions

Readers on

mendeley
440 Mendeley
Title
Real-time tracking of self-reported symptoms to predict potential COVID-19
Published in
Nature Medicine, May 2020
DOI 10.1038/s41591-020-0916-2
Pubmed ID
Authors

Cristina Menni, Ana M. Valdes, Maxim B. Freidin, Carole H. Sudre, Long H. Nguyen, David A. Drew, Sajaysurya Ganesh, Thomas Varsavsky, M. Jorge Cardoso, Julia S. El-Sayed Moustafa, Alessia Visconti, Pirro Hysi, Ruth C. E. Bowyer, Massimo Mangino, Mario Falchi, Jonathan Wolf, Sebastien Ourselin, Andrew T. Chan, Claire J. Steves, Tim D. Spector

Twitter Demographics

The data shown below were collected from the profiles of 3,898 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 440 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 440 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 71 16%
Student > Ph. D. Student 54 12%
Student > Bachelor 41 9%
Other 38 9%
Professor 37 8%
Other 133 30%
Unknown 66 15%
Readers by discipline Count As %
Medicine and Dentistry 127 29%
Biochemistry, Genetics and Molecular Biology 38 9%
Agricultural and Biological Sciences 23 5%
Engineering 23 5%
Computer Science 21 5%
Other 121 28%
Unknown 87 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 2828. 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 20 October 2020.
All research outputs
#865
of 16,094,294 outputs
Outputs from Nature Medicine
#13
of 7,248 outputs
Outputs of similar age
#156
of 282,816 outputs
Outputs of similar age from Nature Medicine
#7
of 127 outputs
Altmetric has tracked 16,094,294 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 7,248 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 63.6. 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 282,816 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 127 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.