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Explaining the geographic spread of emerging epidemics: a framework for comparing viral phylogenies and environmental landscape data

Overview of attention for article published in BMC Bioinformatics, February 2016
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  • 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 (88th percentile)

Mentioned by

news
1 news outlet
twitter
14 X users
facebook
1 Facebook page

Citations

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101 Dimensions

Readers on

mendeley
147 Mendeley
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1 CiteULike
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Title
Explaining the geographic spread of emerging epidemics: a framework for comparing viral phylogenies and environmental landscape data
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0924-x
Pubmed ID
Authors

Simon Dellicour, Rebecca Rose, Oliver G. Pybus

Abstract

Phylogenetic analysis is now an important tool in the study of viral outbreaks. It can reconstruct epidemic history when surveillance epidemiology data are sparse, and can indicate transmission linkages among infections that may not otherwise be evident. However, a remaining challenge is to develop an analytical framework that can test hypotheses about the effect of environmental variables on pathogen spatial spread. Recent phylogeographic approaches can reconstruct the history of virus dispersal from sampled viral genomes and infer the locations of ancestral infections. Such methods provide a unique source of spatio-temporal information, and are exploited here. We present and apply a new statistical framework that combines genomic and geographic data to test the impact of environmental variables on the mode and tempo of pathogen dispersal during emerging epidemics. First, the spatial history of an emerging pathogen is estimated using standard phylogeographic methods. The inferred dispersal path for each phylogenetic lineage is then assigned a "weight" using environmental data (e.g. altitude, land cover). Next, tests measure the association between each environmental variable and lineage movement. A randomisation procedure is used to assess statistical confidence and we validate this approach using simulated data. We apply our new framework to a set of gene sequences from an epidemic of rabies virus in North American raccoons. We test the impact of six different environmental variables on this epidemic and demonstrate that elevation is associated with a slower rabies spread in a natural population. This study shows that it is possible to integrate genomic and environmental data in order to test hypotheses concerning the mode and tempo of virus dispersal during emerging epidemics.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 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 147 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 1%
United States 1 <1%
United Kingdom 1 <1%
Unknown 143 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 34 23%
Student > Ph. D. Student 29 20%
Student > Master 19 13%
Student > Bachelor 16 11%
Professor 6 4%
Other 21 14%
Unknown 22 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 34%
Veterinary Science and Veterinary Medicine 14 10%
Biochemistry, Genetics and Molecular Biology 12 8%
Environmental Science 10 7%
Immunology and Microbiology 9 6%
Other 24 16%
Unknown 28 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 07 February 2022.
All research outputs
#1,793,346
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#393
of 7,454 outputs
Outputs of similar age
#33,556
of 405,646 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 141 outputs
Altmetric has tracked 23,881,329 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 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 94% 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 405,646 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 141 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.