↓ Skip to main content

Transmission of Equine Influenza Virus during an Outbreak Is Characterized by Frequent Mixed Infections and Loose Transmission Bottlenecks

Overview of attention for article published in PLoS Pathogens, December 2012
Altmetric Badge

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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
policy
1 policy source
twitter
11 X users
facebook
1 Facebook page

Citations

dimensions_citation
58 Dimensions

Readers on

mendeley
136 Mendeley
citeulike
2 CiteULike
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
Transmission of Equine Influenza Virus during an Outbreak Is Characterized by Frequent Mixed Infections and Loose Transmission Bottlenecks
Published in
PLoS Pathogens, December 2012
DOI 10.1371/journal.ppat.1003081
Pubmed ID
Authors

Joseph Hughes, Richard C. Allen, Marc Baguelin, Katie Hampson, Gregory J. Baillie, Debra Elton, J. Richard Newton, Paul Kellam, James L. N. Wood, Edward C. Holmes, Pablo R. Murcia

Abstract

The ability of influenza A viruses (IAVs) to cross species barriers and evade host immunity is a major public health concern. Studies on the phylodynamics of IAVs across different scales - from the individual to the population - are essential for devising effective measures to predict, prevent or contain influenza emergence. Understanding how IAVs spread and evolve during outbreaks is critical for the management of epidemics. Reconstructing the transmission network during a single outbreak by sampling viral genetic data in time and space can generate insights about these processes. Here, we obtained intra-host viral sequence data from horses infected with equine influenza virus (EIV) to reconstruct the spread of EIV during a large outbreak. To this end, we analyzed within-host viral populations from sequences covering 90% of the infected yards. By combining gene sequence analyses with epidemiological data, we inferred a plausible transmission network, in turn enabling the comparison of transmission patterns during the course of the outbreak and revealing important epidemiological features that were not apparent using either approach alone. The EIV populations displayed high levels of genetic diversity, and in many cases we observed distinct viral populations containing a dominant variant and a number of related minor variants that were transmitted between infectious horses. In addition, we found evidence of frequent mixed infections and loose transmission bottlenecks in these naturally occurring populations. These frequent mixed infections likely influence the size of epidemics.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 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 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 5 4%
United States 2 1%
Singapore 1 <1%
Unknown 128 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 37 27%
Student > Ph. D. Student 30 22%
Student > Master 12 9%
Student > Postgraduate 10 7%
Student > Bachelor 8 6%
Other 25 18%
Unknown 14 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 32%
Veterinary Science and Veterinary Medicine 16 12%
Immunology and Microbiology 12 9%
Medicine and Dentistry 10 7%
Mathematics 8 6%
Other 18 13%
Unknown 28 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 27. 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 08 January 2021.
All research outputs
#1,444,126
of 25,411,814 outputs
Outputs from PLoS Pathogens
#1,350
of 9,480 outputs
Outputs of similar age
#12,336
of 288,555 outputs
Outputs of similar age from PLoS Pathogens
#18
of 139 outputs
Altmetric has tracked 25,411,814 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,480 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one has done well, scoring higher than 85% 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 288,555 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 95% of its contemporaries.
We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.