↓ Skip to main content

Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses

Overview of attention for article published in Transboundary & Emerging Diseases, August 2019
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
1 blog
twitter
3 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
29 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
Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses
Published in
Transboundary & Emerging Diseases, August 2019
DOI 10.1111/tbed.13318
Pubmed ID
Authors

Daniel P. Walsh, Ting Fung Ma, Hon S. Ip, Jun Zhu

Twitter Demographics

The data shown below were collected from the profiles of 3 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 21%
Student > Master 5 17%
Student > Bachelor 4 14%
Student > Postgraduate 2 7%
Student > Doctoral Student 2 7%
Other 3 10%
Unknown 7 24%
Readers by discipline Count As %
Medicine and Dentistry 5 17%
Mathematics 3 10%
Agricultural and Biological Sciences 2 7%
Computer Science 2 7%
Veterinary Science and Veterinary Medicine 1 3%
Other 6 21%
Unknown 10 34%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 17 June 2021.
All research outputs
#2,996,520
of 18,890,258 outputs
Outputs from Transboundary & Emerging Diseases
#150
of 1,525 outputs
Outputs of similar age
#60,277
of 273,538 outputs
Outputs of similar age from Transboundary & Emerging Diseases
#6
of 23 outputs
Altmetric has tracked 18,890,258 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,525 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has done well, scoring higher than 89% 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 273,538 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 77% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.