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Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data

Overview of attention for article published in PLoS Computational Biology, March 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

twitter
28 X users

Citations

dimensions_citation
63 Dimensions

Readers on

mendeley
160 Mendeley
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Title
Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data
Published in
PLoS Computational Biology, March 2019
DOI 10.1371/journal.pcbi.1006930
Pubmed ID
Authors

Finlay Campbell, Anne Cori, Neil Ferguson, Thibaut Jombart

X Demographics

X Demographics

The data shown below were collected from the profiles of 28 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 160 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 160 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 17%
Student > Ph. D. Student 19 12%
Student > Doctoral Student 18 11%
Student > Master 17 11%
Student > Bachelor 12 8%
Other 33 21%
Unknown 34 21%
Readers by discipline Count As %
Medicine and Dentistry 22 14%
Agricultural and Biological Sciences 21 13%
Biochemistry, Genetics and Molecular Biology 19 12%
Mathematics 13 8%
Nursing and Health Professions 6 4%
Other 34 21%
Unknown 45 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 28 May 2021.
All research outputs
#2,315,747
of 25,411,814 outputs
Outputs from PLoS Computational Biology
#2,090
of 8,976 outputs
Outputs of similar age
#51,044
of 364,067 outputs
Outputs of similar age from PLoS Computational Biology
#57
of 200 outputs
Altmetric has tracked 25,411,814 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,976 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 well, scoring higher than 76% 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 364,067 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 200 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.