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A novel epidemiological model to better understand and predict the observed seasonal spread of Pestivirus in Pyrenean chamois populations

Overview of attention for article published in Veterinary Research, July 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

twitter
4 tweeters

Readers on

mendeley
31 Mendeley
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Title
A novel epidemiological model to better understand and predict the observed seasonal spread of Pestivirus in Pyrenean chamois populations
Published in
Veterinary Research, July 2015
DOI 10.1186/s13567-015-0218-8
Pubmed ID
Authors

Gaël Beaunée, Emmanuelle Gilot-Fromont, Mathieu Garel, Pauline Ezanno

Abstract

Seasonal variations in individual contacts give rise to a complex interplay between host demography and pathogen transmission. This is particularly true for wild populations, which highly depend on their natural habitat. These seasonal cycles induce variations in pathogen transmission. The seasonality of these biological processes should therefore be considered to better represent and predict pathogen spread. In this study, we sought to better understand how the seasonality of both the demography and social contacts of a mountain ungulate population impacts the spread of a pestivirus within, and the dynamics of, this population. We propose a mathematical model to represent this complex biological system. The pestivirus can be transmitted both horizontally through direct contact and vertically in utero. Vertical transmission leads to abortion or to the birth of persistently infected animals with a short life expectancy. Horizontal transmission involves a complex dynamics because of seasonal variations in contact among sexes and age classes. We performed a sensitivity analysis that identified transmission rates and disease-related mortality as key parameters. We then used data from a long-term demographic and epidemiological survey of the studied population to estimate these mostly unknown epidemiological parameters. Our model adequately represents the system dynamics, observations and model predictions showing similar seasonal patterns. We show that the virus has a significant impact on population dynamics, and that persistently infected animals play a major role in the epidemic dynamics. Modeling the seasonal dynamics allowed us to obtain realistic prediction and to identify key parameters of transmission.

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

Geographical breakdown

Country Count As %
France 1 3%
Unknown 30 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 35%
Researcher 7 23%
Student > Postgraduate 2 6%
Student > Bachelor 2 6%
Student > Doctoral Student 1 3%
Other 3 10%
Unknown 5 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 35%
Veterinary Science and Veterinary Medicine 6 19%
Environmental Science 4 13%
Biochemistry, Genetics and Molecular Biology 1 3%
Social Sciences 1 3%
Other 0 0%
Unknown 8 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 14 April 2016.
All research outputs
#1,690,431
of 7,551,260 outputs
Outputs from Veterinary Research
#110
of 562 outputs
Outputs of similar age
#60,901
of 226,073 outputs
Outputs of similar age from Veterinary Research
#7
of 39 outputs
Altmetric has tracked 7,551,260 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 562 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done well, scoring higher than 78% 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 226,073 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 72% of its contemporaries.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.