Hierarchical Bayesian model for prevalence inferences and determination of a country's status for an animal pathogen.
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.
Suess
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Department of Statistics, California State University, Hayward, CA 94542, USA. esuess@csuhayward.edu
Gardner
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Johnson
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WO
D000818:Animals; D000829:Animals, Domestic / Q000382:microbiology / Q000821:virology; D001499:Bayes Theorem; D003198:Computer Simulation; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D009521:Newcastle Disease / Q000453:epidemiology*; D019318:Porcine Reproductive and Respiratory Syndrome / Q000191:economics / Q000453:epidemiology*; D011200:Poultry / Q000821:virology; D015995:Prevalence; D013552:Swine / Q000821:virology; D013553:Swine Diseases / Q000453:epidemiology; D013557:Switzerland / Q000453:epidemiology