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Spatial Modeling of Wild Bird Risk Factors for Highly Pathogenic A(H5N1) Avian Influenza Virus Transmission

Overview of attention for article published in Avian Diseases, May 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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4 tweeters
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1 Facebook page

Citations

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5 Dimensions

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24 Mendeley
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Title
Spatial Modeling of Wild Bird Risk Factors for Highly Pathogenic A(H5N1) Avian Influenza Virus Transmission
Published in
Avian Diseases, May 2016
DOI 10.1637/11125-050615-reg
Pubmed ID
Authors

Prosser, Diann J, Hungerford, Laura L, Erwin, R Michael, Ottinger, Mary Ann, Takekawa, John Y, Newman, Scott H, Xiao, Xiangming, Ellis, Erle C

Abstract

One of the longest-persisting avian influenza viruses in history, highly pathogenic avian influenza virus (HPAIV) A(H5N1), continues to evolve after 18 yr, advancing the threat of a global pandemic. Wild waterfowl (family Anatidae) are reported as secondary transmitters of HPAIV and primary reservoirs for low-pathogenic avian influenza viruses, yet spatial inputs for disease risk modeling for this group have been lacking. Using geographic information software and Monte Carlo simulations, we developed geospatial indices of waterfowl abundance at 1 and 30 km resolutions and for the breeding and wintering seasons for China, the epicenter of H5N1. Two spatial layers were developed: cumulative waterfowl abundance (WAB), a measure of predicted abundance across species, and cumulative abundance weighted by H5N1 prevalence (WPR), whereby abundance for each species was adjusted based on prevalence values and then totaled across species. Spatial patterns of the model output differed between seasons, with higher WAB and WPR in the northern and western regions of China for the breeding season and in the southeast for the wintering season. Uncertainty measures indicated highest error in southeastern China for both WAB and WPR. We also explored the effect of resampling waterfowl layers from 1 to 30 km resolution for multiscale risk modeling. Results indicated low average difference (less than 0.16 and 0.01 standard deviations for WAB and WPR, respectively), with greatest differences in the north for the breeding season and southeast for the wintering season. This work provides the first geospatial models of waterfowl abundance available for China. The indices provide important inputs for modeling disease transmission risk at the interface of poultry and wild birds. These models are easily adaptable, have broad utility to both disease and conservation needs, and will be available to the scientific community for advanced modeling applications.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Unknown 23 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 29%
Unspecified 6 25%
Student > Ph. D. Student 3 13%
Researcher 3 13%
Student > Doctoral Student 2 8%
Other 3 13%
Readers by discipline Count As %
Unspecified 7 29%
Agricultural and Biological Sciences 7 29%
Veterinary Science and Veterinary Medicine 4 17%
Medicine and Dentistry 2 8%
Arts and Humanities 1 4%
Other 3 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 21 June 2016.
All research outputs
#3,489,111
of 7,926,939 outputs
Outputs from Avian Diseases
#144
of 428 outputs
Outputs of similar age
#109,400
of 263,089 outputs
Outputs of similar age from Avian Diseases
#4
of 10 outputs
Altmetric has tracked 7,926,939 research outputs across all sources so far. This one has received more attention than most of these and is in the 54th percentile.
So far Altmetric has tracked 428 research outputs from this source. They receive a mean Attention Score of 1.6. This one has gotten more attention than average, scoring higher than 65% 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 263,089 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.