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A global model of avian influenza prediction in wild birds: the importance of northern regions

Overview of attention for article published in Veterinary Research, June 2013
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

blogs
1 blog
twitter
13 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
58 Dimensions

Readers on

mendeley
88 Mendeley
citeulike
1 CiteULike
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Title
A global model of avian influenza prediction in wild birds: the importance of northern regions
Published in
Veterinary Research, June 2013
DOI 10.1186/1297-9716-44-42
Pubmed ID
Authors

Keiko A Herrick, Falk Huettmann, Michael A Lindgren

Abstract

Avian influenza virus (AIV) is enzootic to wild birds, which are its natural reservoir. The virus exhibits a large degree of genetic diversity and most of the isolated strains are of low pathogenicity to poultry. Although AIV is nearly ubiquitous in wild bird populations, highly pathogenic H5N1 subtypes in poultry have been the focus of most modeling efforts. To better understand viral ecology of AIV, a predictive model should 1) include wild birds, 2) include all isolated subtypes, and 3) cover the host's natural range, unbounded by artificial country borders. As of this writing, there are few large-scale predictive models of AIV in wild birds. We used the Random Forests algorithm, an ensemble data-mining machine-learning method, to develop a global-scale predictive map of AIV, identify important predictors, and describe the environmental niche of AIV in wild bird populations. The model has an accuracy of 0.79 and identified northern areas as having the highest relative predicted risk of outbreak. The primary niche was described as regions of low annual rainfall and low temperatures. This study is the first global-scale model of low-pathogenicity avian influenza in wild birds and underscores the importance of largely unstudied northern regions in the persistence of AIV.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
United Kingdom 1 1%
Spain 1 1%
Vietnam 1 1%
Unknown 83 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 25%
Researcher 21 24%
Student > Master 10 11%
Student > Doctoral Student 6 7%
Student > Bachelor 5 6%
Other 18 20%
Unknown 6 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 33%
Environmental Science 8 9%
Medicine and Dentistry 8 9%
Mathematics 6 7%
Computer Science 5 6%
Other 24 27%
Unknown 8 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 January 2022.
All research outputs
#1,721,187
of 21,576,978 outputs
Outputs from Veterinary Research
#53
of 1,151 outputs
Outputs of similar age
#14,717
of 175,094 outputs
Outputs of similar age from Veterinary Research
#1
of 6 outputs
Altmetric has tracked 21,576,978 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,151 research outputs from this source. They receive a mean Attention Score of 4.6. 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 175,094 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 91% of its contemporaries.
We're also able to compare this research output to 6 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