Hierarchical Bayesian model for prevalence inferences and determination of a country's status for an animal pathogen

E. A. Suess, Ian Gardner, W. O. Johnson

Research output: Contribution to journalArticle

43 Scopus citations

Abstract

Certification that a country, region or state is "free" from a pathogen or has a prevalence less than a threshold value has implications for trade in animals and animal products. We develop a Bayesian model for assessment of (i) the probability that a country is "free" of or has an animal pathogen, (ii) the proportion of infected herds in an infected country, and (iii) the within-herd prevalence in infected herds. The model uses test results from animals sampled in a two-stage cluster sample of herds within a country. Model parameters are estimated using modern Markov-chain Monte Carlo methods. We demonstrate our approach using published data from surveys of Newcastle disease and porcine reproductive and respiratory syndrome in Switzerland, and for three simulated data sets.

Original languageEnglish (US)
Pages (from-to)155-171
Number of pages17
JournalPreventive Veterinary Medicine
Volume55
Issue number3
DOIs
StatePublished - Oct 15 2002

Keywords

  • Adaptive rejection sampling
  • Bayesian modeling
  • Disease freedom
  • Gibbs sampler
  • Herd-level testing
  • Markov-chain Monte Carlo methods
  • Newcastle disease
  • Porcine reproductive and respiratory syndrome

ASJC Scopus subject areas

  • Food Animals
  • Animal Science and Zoology

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