PropertyValue
?:abstract
  • A common research task in COVID-19 studies often involves the prevalence estimation of certain medical outcomes. Although point estimates with confidence intervals are typically obtained, a better approach is to estimate the entire posterior probability distribution of the prevalence, which can be easily accomplished with a standard Bayesian approach using binomial likelihood and its conjugate beta prior distribution. Using two recently published COVID-19 data sets, we performed Bayesian analysis to estimate the prevalence of infection fatality in Iceland and asymptomatic children in the United States.
is ?:annotates of
?:creator
?:doi
?:doi
  • 10.1093/jamiaopen/ooaa062
?:externalLink
?:journal
  • JAMIA_Open
?:license
  • cc-by-nc
?:pdf_json_files
  • document_parses/pdf_json/616bf09c007c5b025bed2ef107d6b25d07039557.json
?:pmc_json_files
  • document_parses/pmc_json/PMC7750711.xml.json
?:pmcid
?:publication_isRelatedTo_Disease
?:sha_id
?:source
  • PMC
?:title
  • A primer on Bayesian estimation of prevalence of COVID-19 patient outcomes
?:type
?:year
  • 2020-11-10

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