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Mendeley readers
Attention Score in Context
Title |
Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic
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Published in |
PLOS ONE, August 2011
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DOI | 10.1371/journal.pone.0023610 |
Pubmed ID | |
Authors |
Samantha Cook, Corrie Conrad, Ashley L. Fowlkes, Matthew H. Mohebbi |
Abstract |
Google Flu Trends (GFT) uses anonymized, aggregated internet search activity to provide near-real time estimates of influenza activity. GFT estimates have shown a strong correlation with official influenza surveillance data. The 2009 influenza virus A (H1N1) pandemic [pH1N1] provided the first opportunity to evaluate GFT during a non-seasonal influenza outbreak. In September 2009, an updated United States GFT model was developed using data from the beginning of pH1N1. |
X Demographics
The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 26% |
Japan | 3 | 16% |
Unknown | 11 | 58% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 11 | 58% |
Scientists | 5 | 26% |
Practitioners (doctors, other healthcare professionals) | 2 | 11% |
Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 317 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 2% |
United Kingdom | 5 | 2% |
Canada | 3 | <1% |
Brazil | 2 | <1% |
Netherlands | 1 | <1% |
Israel | 1 | <1% |
Portugal | 1 | <1% |
Spain | 1 | <1% |
Saudi Arabia | 1 | <1% |
Other | 0 | 0% |
Unknown | 296 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 82 | 26% |
Researcher | 50 | 16% |
Student > Master | 41 | 13% |
Student > Bachelor | 29 | 9% |
Professor > Associate Professor | 17 | 5% |
Other | 60 | 19% |
Unknown | 38 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 62 | 20% |
Medicine and Dentistry | 49 | 15% |
Social Sciences | 28 | 9% |
Agricultural and Biological Sciences | 18 | 6% |
Engineering | 16 | 5% |
Other | 82 | 26% |
Unknown | 62 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 156. 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 15 March 2022.
All research outputs
#266,758
of 25,670,640 outputs
Outputs from PLOS ONE
#3,834
of 223,929 outputs
Outputs of similar age
#882
of 134,830 outputs
Outputs of similar age from PLOS ONE
#37
of 2,456 outputs
Altmetric has tracked 25,670,640 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 223,929 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.8. This one has done particularly well, scoring higher than 98% 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 134,830 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 2,456 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 98% of its contemporaries.