Literature DB >> 27237395

Bayesian estimation of prevalence of paratuberculosis in dairy herds enrolled in a voluntary Johne's Disease Control Programme in Ireland.

Conor G McAloon1, Michael L Doherty2, Paul Whyte2, Luke O'Grady2, Simon J More2, Locksley L McV Messam2, Margaret Good3, Peter Mullowney3, Sam Strain4, Martin J Green5.   

Abstract

Bovine paratuberculosis is a disease characterised by chronic granulomatous enteritis which manifests clinically as a protein-losing enteropathy causing diarrhoea, hypoproteinaemia, emaciation and, eventually death. Some evidence exists to suggest a possible zoonotic link and a national voluntary Johne's Disease Control Programme was initiated by Animal Health Ireland in 2013. The objective of this study was to estimate herd-level true prevalence (HTP) and animal-level true prevalence (ATP) of paratuberculosis in Irish herds enrolled in the national voluntary JD control programme during 2013-14. Two datasets were used in this study. The first dataset had been collected in Ireland during 2005 (5822 animals from 119 herds), and was used to construct model priors. Model priors were updated with a primary (2013-14) dataset which included test records from 99,101 animals in 1039 dairy herds and was generated as part of the national voluntary JD control programme. The posterior estimate of HTP from the final Bayesian model was 0.23-0.34 with a 95% probability. Across all herds, the median ATP was found to be 0.032 (0.009, 0.145). This study represents the first use of Bayesian methodology to estimate the prevalence of paratuberculosis in Irish dairy herds. The HTP estimate was higher than previous Irish estimates but still lower than estimates from other major dairy producing countries.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Bayesian; Dairy; Ireland; Paratuberculosis; Prevalence

Mesh:

Year:  2016        PMID: 27237395     DOI: 10.1016/j.prevetmed.2016.04.014

Source DB:  PubMed          Journal:  Prev Vet Med        ISSN: 0167-5877            Impact factor:   2.670


  10 in total

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  10 in total

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