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A review of influenza detection and prediction through social networking sites

Overview of attention for article published in Theoretical Biology and Medical Modelling, February 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#10 of 279)
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

news
3 news outlets
blogs
1 blog
twitter
10 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
62 Dimensions

Readers on

mendeley
178 Mendeley
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Title
A review of influenza detection and prediction through social networking sites
Published in
Theoretical Biology and Medical Modelling, February 2018
DOI 10.1186/s12976-017-0074-5
Pubmed ID
Authors

Ali Alessa, Miad Faezipour

Abstract

Early prediction of seasonal epidemics such as influenza may reduce their impact in daily lives. Nowadays, the web can be used for surveillance of diseases. Search engines and social networking sites can be used to track trends of different diseases seven to ten days faster than government agencies such as Center of Disease Control and Prevention (CDC). CDC uses the Illness-Like Influenza Surveillance Network (ILINet), which is a program used to monitor Influenza-Like Illness (ILI) sent by thousands of health care providers in order to detect influenza outbreaks. It is a reliable tool, however, it is slow and expensive. For that reason, many studies aim to develop methods that do real time analysis to track ILI using social networking sites. Social media data such as Twitter can be used to predict the spread of flu in the population and can help in getting early warnings. Today, social networking sites (SNS) are used widely by many people to share thoughts and even health status. Therefore, SNS provides an efficient resource for disease surveillance and a good way to communicate to prevent disease outbreaks. The goal of this study is to review existing alternative solutions that track flu outbreak in real time using social networking sites and web blogs. Many studies have shown that social networking sites can be used to conduct real time analysis for better predictions.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 178 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 178 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 30 17%
Student > Ph. D. Student 29 16%
Student > Bachelor 17 10%
Other 13 7%
Researcher 10 6%
Other 37 21%
Unknown 42 24%
Readers by discipline Count As %
Computer Science 41 23%
Medicine and Dentistry 22 12%
Engineering 9 5%
Physics and Astronomy 6 3%
Social Sciences 6 3%
Other 41 23%
Unknown 53 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 36. 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 02 June 2020.
All research outputs
#773,760
of 19,088,857 outputs
Outputs from Theoretical Biology and Medical Modelling
#10
of 279 outputs
Outputs of similar age
#23,220
of 384,534 outputs
Outputs of similar age from Theoretical Biology and Medical Modelling
#1
of 1 outputs
Altmetric has tracked 19,088,857 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 279 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has done particularly well, scoring higher than 96% 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 384,534 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 93% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them