Sample Size Calculations for Surveys to Substantiate Freedom of Populations from Infectious Agents

Wesley O. Johnson, Chun Lung Su, Ian Gardner, Ronald Christensen

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


We develop a Bayesian approach to sample size computations for surveys designed to provide evidence of freedom from a disease or from an infectious agent. A population is considered "disease-free" when the prevalence or probability of disease is less than some threshold value. Prior distributions are specified for diagnostic test sensitivity and specificity and we test the null hypothesis that the prevalence is below the threshold. Sample size computations are developed using hypergeometric sampling for finite populations and binomial sampling for infinite populations. A normal approximation is also developed. Our procedures are compared with the frequentist methods of Cameron and Baldock (1998a, Preventive Veterinary Medicine 34, 1-17.) using an example of foot-and-mouth disease. User-friendly programs for sample size calculation and analysis of survey data are available at

Original languageEnglish (US)
Pages (from-to)165-171
Number of pages7
Issue number1
StatePublished - Mar 1 2004


  • Bayes
  • Gibbs sampling
  • Hypergeometric distribution
  • Prediction
  • Prevalence
  • Risk analysis
  • Sensitivity
  • Specificity

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Immunology and Microbiology(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


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