↓ Skip to main content

Mapping Avian Influenza Transmission Risk at the Interface of Domestic Poultry and Wild Birds

Overview of attention for article published in Frontiers in Public Health, January 2013
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
5 X users

Citations

dimensions_citation
50 Dimensions

Readers on

mendeley
70 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
Mapping Avian Influenza Transmission Risk at the Interface of Domestic Poultry and Wild Birds
Published in
Frontiers in Public Health, January 2013
DOI 10.3389/fpubh.2013.00028
Pubmed ID
Authors

Diann J. Prosser, Laura L. Hungerford, R. Michael Erwin, Mary Ann Ottinger, John Y. Takekawa, Erle C. Ellis

Abstract

Emergence of avian influenza viruses with high lethality to humans, such as the currently circulating highly pathogenic A(H5N1) (emerged in 1996) and A(H7N9) cause serious concern for the global economic and public health sectors. Understanding the spatial and temporal interface between wild and domestic populations, from which these viruses emerge, is fundamental to taking action. This information, however, is rarely considered in influenza risk models, partly due to a lack of data. We aim to identify areas of high transmission risk between domestic poultry and wild waterfowl in China, the epicenter of both viruses. Two levels of models were developed: one that predicts hotspots of novel virus emergence between domestic and wild birds, and one that incorporates H5N1 risk factors, for which input data exists. Models were produced at 1 and 30 km spatial resolution, and two temporal seasons. Patterns of risk varied between seasons with higher risk in the northeast, central-east, and western regions of China during spring and summer, and in the central and southeastern regions during winter. Monte-Carlo uncertainty analyses indicated varying levels of model confidence, with lowest errors in the densely populated regions of eastern and southern China. Applications and limitations of the models are discussed within.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 2 3%
United Kingdom 1 1%
Unknown 67 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 29%
Researcher 14 20%
Student > Master 7 10%
Professor 4 6%
Student > Doctoral Student 3 4%
Other 10 14%
Unknown 12 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 33%
Veterinary Science and Veterinary Medicine 9 13%
Medicine and Dentistry 9 13%
Environmental Science 6 9%
Business, Management and Accounting 2 3%
Other 8 11%
Unknown 13 19%
Attention Score in Context

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 13 September 2013.
All research outputs
#13,893,979
of 22,716,996 outputs
Outputs from Frontiers in Public Health
#3,234
of 9,695 outputs
Outputs of similar age
#164,392
of 280,757 outputs
Outputs of similar age from Frontiers in Public Health
#33
of 67 outputs
Altmetric has tracked 22,716,996 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,695 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.8. This one has gotten more attention than average, scoring higher than 64% 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 280,757 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 67 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.