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Controlling disease outbreaks in wildlife using limited culling: modelling classical swine fever incursions in wild pigs in Australia

Overview of attention for article published in Veterinary Research, January 2012
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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 (90th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

twitter
11 tweeters
wikipedia
2 Wikipedia pages

Readers on

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89 Mendeley
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Title
Controlling disease outbreaks in wildlife using limited culling: modelling classical swine fever incursions in wild pigs in Australia
Published in
Veterinary Research, January 2012
DOI 10.1186/1297-9716-43-3
Pubmed ID
Authors

Brendan D Cowled, M Graeme Garner, Katherine Negus, Michael P Ward

Abstract

Disease modelling is one approach for providing new insights into wildlife disease epidemiology. This paper describes a spatio-temporal, stochastic, susceptible- exposed-infected-recovered process model that simulates the potential spread of classical swine fever through a documented, large and free living wild pig population following a simulated incursion. The study area (300 000 km2) was in northern Australia. Published data on wild pig ecology from Australia, and international Classical Swine Fever data was used to parameterise the model. Sensitivity analyses revealed that herd density (best estimate 1-3 pigs km-2), daily herd movement distances (best estimate approximately 1 km), probability of infection transmission between herds (best estimate 0.75) and disease related herd mortality (best estimate 42%) were highly influential on epidemic size but that extraordinary movements of pigs and the yearly home range size of a pig herd were not. CSF generally established (98% of simulations) following a single point introduction. CSF spread at approximately 9 km2 per day with low incidence rates (< 2 herds per day) in an epidemic wave along contiguous habitat for several years, before dying out (when the epidemic arrived at the end of a contiguous sub-population or at a low density wild pig area). The low incidence rate indicates that surveillance for wildlife disease epidemics caused by short lived infections will be most efficient when surveillance is based on detection and investigation of clinical events, although this may not always be practical. Epidemics could be contained and eradicated with culling (aerial shooting) or vaccination when these were adequately implemented. It was apparent that the spatial structure, ecology and behaviour of wild populations must be accounted for during disease management in wildlife. An important finding was that it may only be necessary to cull or vaccinate relatively small proportions of a population to successfully contain and eradicate some wildlife disease epidemics.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
United Kingdom 3 3%
Chile 1 1%
Germany 1 1%
Australia 1 1%
Unknown 80 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 36%
Student > Ph. D. Student 12 13%
Student > Master 9 10%
Student > Bachelor 9 10%
Other 4 4%
Other 15 17%
Unknown 8 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 38%
Medicine and Dentistry 11 12%
Veterinary Science and Veterinary Medicine 9 10%
Environmental Science 7 8%
Immunology and Microbiology 3 3%
Other 9 10%
Unknown 16 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 30 January 2021.
All research outputs
#2,696,764
of 22,514,578 outputs
Outputs from Veterinary Research
#106
of 1,188 outputs
Outputs of similar age
#24,536
of 251,530 outputs
Outputs of similar age from Veterinary Research
#5
of 10 outputs
Altmetric has tracked 22,514,578 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,188 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 91% 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 251,530 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 90% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.