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Landscape and Regional Environmental Analysis of the Spatial Distribution of Hantavirus Human Cases in Europe

Overview of attention for article published in Frontiers in Public Health, March 2015
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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Title
Landscape and Regional Environmental Analysis of the Spatial Distribution of Hantavirus Human Cases in Europe
Published in
Frontiers in Public Health, March 2015
DOI 10.3389/fpubh.2015.00054
Pubmed ID
Authors

Caroline Brigitte Zeimes, Sophie Quoilin, Heikki Henttonen, Outi Lyytikäinen, Olli Vapalahti, Jean-Marc Reynes, Chantal Reusken, Arno N. Swart, Kirsti Vainio, Marika Hjertqvist, Sophie O. Vanwambeke

Abstract

In Europe, the most prevalent hantavirus, Puumala virus, is transmitted by bank voles and causes nephropathia epidemica in human. The European spatial distribution of nephropathia epidemica is investigated here for the first time with a rich set of environmental variables. The influence of variables at the landscape and regional level is studied through multilevel logistic regression, and further information on their effects across the different European ecoregions is obtained by comparing an overall niche model (boosted regression trees) with regressions by ecoregion. The presence of nephropathia epidemica is likely in populated regions with well-connected forests, more intense vegetation activity, low soil water content, mild summers, and cold winters. In these regions, landscapes with a higher proportion of built-up areas in forest ecotones and lower minimum temperature in winter are expected to be more at risk. Climate and forest connectivity have a stronger effect at the regional level. If variables are staying at their current values, the models predict that nephropathia epidemica may know intensification but should not spread (although southern Sweden, the Norwegian coast, and the Netherlands should be kept under watch). Models indicate that large-scale modeling can lead to a very high predictive power. At large scale, the effect of one variable on disease may follow three response scenarios: the effect may be the same across the entire study area, the effect can change according to the variable value, and the effect can change depending on local specificities. Each of these scenarios impacts large-scale modeling differently.

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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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 1 2%
France 1 2%
Unknown 56 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 21%
Student > Ph. D. Student 10 17%
Researcher 8 14%
Student > Doctoral Student 3 5%
Other 3 5%
Other 8 14%
Unknown 14 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 26%
Medicine and Dentistry 8 14%
Environmental Science 7 12%
Veterinary Science and Veterinary Medicine 3 5%
Mathematics 2 3%
Other 9 16%
Unknown 14 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 17 June 2015.
All research outputs
#13,197,285
of 22,797,621 outputs
Outputs from Frontiers in Public Health
#2,848
of 9,796 outputs
Outputs of similar age
#124,547
of 264,714 outputs
Outputs of similar age from Frontiers in Public Health
#24
of 70 outputs
Altmetric has tracked 22,797,621 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,796 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 10.0. This one has gotten more attention than average, scoring higher than 69% 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 264,714 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 52% of its contemporaries.
We're also able to compare this research output to 70 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.