Extrapolating Sentinel Surveillance Information to Estimate National COVID Hospital Admission Rates: A Bayesian Modeling Approach.

ABSTRACT: The COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) was established in March 2020 to monitor trends in hospitalizations associated with SARS-CoV-2 infection. COVID-NET is a geographically diverse population-based surveillance system for laboratory-confirmed COVID-19-associated hospitalizations with a combined catchment area covering approximately 10% of the US population. Data collected in COVID-NET includes monthly counts of hospitalizations for persons with confirmed SARS-CoV-2 infection who reside within the defined catchment area. A Bayesian modeling approach is proposed to estimate US national COVID-associated hospital admission rates based on information reported in the COVID-NET system. A key component of the approach is the ability to estimate uncertainty resul

SUBMITTER: Devine O 

PROVIDER: S-EPMC11497105 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature