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Inferring the effectiveness of government interventions against COVID-19

Overview of attention for article published in Science, December 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 (#12 of 83,908)
  • 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)

Citations

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845 Dimensions

Readers on

mendeley
893 Mendeley
Title
Inferring the effectiveness of government interventions against COVID-19
Published in
Science, December 2020
DOI 10.1126/science.abd9338
Pubmed ID
Authors

Jan M Brauner, Sören Mindermann, Mrinank Sharma, David Johnston, John Salvatier, Tomáš Gavenčiak, Anna B Stephenson, Gavin Leech, George Altman, Vladimir Mikulik, Alexander John Norman, Joshua Teperowski Monrad, Tamay Besiroglu, Hong Ge, Meghan A Hartwick, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal, Jan Kulveit

X Demographics

X Demographics

The data shown below were collected from the profiles of 13,088 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 893 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 893 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 142 16%
Student > Ph. D. Student 95 11%
Student > Master 87 10%
Student > Bachelor 69 8%
Other 47 5%
Other 169 19%
Unknown 284 32%
Readers by discipline Count As %
Medicine and Dentistry 96 11%
Agricultural and Biological Sciences 52 6%
Biochemistry, Genetics and Molecular Biology 45 5%
Social Sciences 44 5%
Computer Science 35 4%
Other 285 32%
Unknown 336 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9101. 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 23 June 2024.
All research outputs
#237
of 26,369,714 outputs
Outputs from Science
#12
of 83,908 outputs
Outputs of similar age
#19
of 533,813 outputs
Outputs of similar age from Science
#2
of 892 outputs
Altmetric has tracked 26,369,714 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 83,908 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 66.7. 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 533,813 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 892 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.