Resource Pack: Diagnostic Tests, Bayes, and COVID-19
2023
This resource pack provides a curated set of articles, perspectives, and interactives about diagnostic testing for COVID-19. The pack provides materials that will be particularly useful for educators who are teaching diagnostic test performance, value of information, and probability revision using Bayes’ theorem.
The majority of papers focus on PCR or rapid antigen testing on samples obtained from the respiratory tract by nasopharyngeal swab. The mechanism of false negative results (e.g., timing of sample collection in relation to illness onset, deficiency in sampling technique, etc.), and less commonly occurring false positive results (e.g., technical errors and reagent contamination) are discussed. Several articles quantitatively illustrate the Bayesian approach, using information on the prior probability (prevalence), the probability of a positive test given disease (sensitivity) and the probability of a negative test given no disease (specificity) to calculate the post-test probability of COVID-19. Several papers include illustrations and graphics, as well as interactives, to illustrate these relationships and provide quantitative insight.
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Source:
Resource Pack: Diagnostic Tests, Bayes, and COVID-19. Center for Health Decision Science, Harvard T.H. Chan School of Public Health 2023. https://repository.chds.hsph.harvard.edu/repository/collection/resource-pack-diagnostic-tests-bayes-and-covid-19