↓ Skip to main content

Synergistic interventions to control COVID-19: Mass testing and isolation mitigates reliance on distancing

Overview of attention for article published in PLoS Computational Biology, October 2021
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
6 news outlets
policy
1 policy source
twitter
44 X users
reddit
1 Redditor

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
45 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Synergistic interventions to control COVID-19: Mass testing and isolation mitigates reliance on distancing
Published in
PLoS Computational Biology, October 2021
DOI 10.1371/journal.pcbi.1009518
Pubmed ID
Authors

Emily Howerton, Matthew J. Ferrari, Ottar N. Bjørnstad, Tiffany L. Bogich, Rebecca K. Borchering, Chris P. Jewell, James D. Nichols, William J. M. Probert, Michael C. Runge, Michael J. Tildesley, Cécile Viboud, Katriona Shea

X Demographics

X Demographics

The data shown below were collected from the profiles of 44 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 11%
Student > Ph. D. Student 5 11%
Researcher 3 7%
Student > Doctoral Student 2 4%
Lecturer 1 2%
Other 4 9%
Unknown 25 56%
Readers by discipline Count As %
Medicine and Dentistry 4 9%
Biochemistry, Genetics and Molecular Biology 3 7%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Unspecified 1 2%
Veterinary Science and Veterinary Medicine 1 2%
Other 7 16%
Unknown 27 60%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 75. 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 06 February 2023.
All research outputs
#579,141
of 25,584,565 outputs
Outputs from PLoS Computational Biology
#423
of 9,004 outputs
Outputs of similar age
#14,022
of 443,354 outputs
Outputs of similar age from PLoS Computational Biology
#14
of 220 outputs
Altmetric has tracked 25,584,565 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,004 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done particularly well, scoring higher than 95% 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 443,354 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 220 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.