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The Limits of Test-Based Scrapie Eradication Programs in Goats

Overview of attention for article published in PLOS ONE, January 2013
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Title
The Limits of Test-Based Scrapie Eradication Programs in Goats
Published in
PLOS ONE, January 2013
DOI 10.1371/journal.pone.0054911
Pubmed ID
Authors

Fabien Corbière, Cécile Chauvineau-Perrin, Caroline Lacroux, Séverine Lugan, Pierrette Costes, Myriam Thomas, Isabelle Brémaud, Christophe Chartier, Francis Barillet, François Schelcher, Olivier Andréoletti

Abstract

Small ruminant post-mortem testing programs were initially designed for monitoring the prevalence of prion disease. They are now considered as a potential alternative to genetic selection for eradicating/controlling classical scrapie at population level. If such policy should be implemented, its success would be crucially dependent on the efficiency of the surveillance system used to identify infected flocks. In this study, we first determined the performance of post-mortem classical scrapie detection in eight naturally affected goat herds (total n = 1961 animals) according to the age at culling. These results provided us with necessary parameters to estimate, through a Monte Carlo simulation model, the performance of scrapie detection in a commercial population. According to this model, whatever the number of tests performed, post mortem surveillance will have limited success in identifying infected herds. These data support the contention that scrapie eradication programs relying solely on post mortem testing in goats will probably fail. Considering the epidemiological and pathological similarities of scrapie in sheep and goats, the efficiency of scrapie surveillance in both species is likely to be similar.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 25%
Student > Ph. D. Student 3 13%
Professor > Associate Professor 2 8%
Student > Doctoral Student 2 8%
Professor 2 8%
Other 7 29%
Unknown 2 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 42%
Biochemistry, Genetics and Molecular Biology 4 17%
Medicine and Dentistry 2 8%
Unspecified 1 4%
Veterinary Science and Veterinary Medicine 1 4%
Other 3 13%
Unknown 3 13%