www.uhasselt.be
DSpace

Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11841

Title: Estimating herd prevalence on the basis of aggregate testing of animals
Authors: FAES, Christel
AERTS, Marc
LITIERE, Saskia
Meroc, Estelle
Van der Stede, Yves
Mintiens, Koen
Issue Date: 2011
Publisher: WILEY-BLACKWELL
Citation: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, 174. p. 155-174
Abstract: It is common practice that some or all animals in a group of animals, e. g. a herd, are tested for their health status by using a diagnostic test to investigate whether the herd is infected by a disease. Several obstacles complicate the estimation of herd prevalence on the basis of test results of the animals. First, diagnostic tests are often imperfect, resulting in a misclassification of the animal's disease status. It is well known how to correct the animal's apparent prevalence by using the diagnostic sensitivity and specificity of the animal test, but the effects on herd prevalence are less clear. Second, in practice, a herd is often defined as positive when at least one sampled animal tested positively. This definition is ambiguous and is also different from the herd prevalence that is based on having at least one diseased animal in the herd. The paper provides a discussion of these aspects and proposes a method to estimate the true herd prevalence on the basis of the health status of ( all or a sample of) animals within a herd corrected for the sensitivity and specificity of the individual test, the number of animals that are tested in the herd and the uncertainty of the diagnostic test characteristics.
Notes: [Faes, Christel] Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. [Meroc, Estelle; Van der Stede, Yves] Vet & Agrochem Res Ctr, Uccle, Belgium. [Mintiens, Koen] Vose Consulting, Ghent, Belgium. christel.faes@uhasselt.be
URI: http://hdl.handle.net/1942/11841
DOI: 10.1111/j.1467-985X.2010.00652.x
ISI #: 000285969600010
ISSN: 0964-1998
Category: A1
Type: Journal Contribution
Validation: ecoom, 2012
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
published version1.09 MBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.