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. 2024 Oct;18(10):e70026.
doi: 10.1111/irv.70026.

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

Owen Devine  1 , Huong Pham  2 , Betsy Gunnels  3 , Heather E Reese  4 , Molly Steele  4 , Alexia Couture  2 , Danielle Iuliano  2 , Darpun Sachdev  5 , Nisha B Alden  6 , James Meek  7 , Lucy Witt  8 , Patricia A Ryan  9 , Libby Reeg  10 , Ruth Lynfield  11 , Susan L Ropp  12 , Grant Barney  13 , Brenda L Tesini  14 , Eli Shiltz  15 , Melissa Sutton  16 , H Keipp Talbot  17 , Isabella Reyes  18 , Fiona P Havers  2
Affiliations

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

Owen Devine et al. Influenza Other Respir Viruses. 2024 Oct.

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 resulting from extrapolation of hospitalization rates observed within COVID-NET to the US population. In addition, the proposed model enables estimation of other contributors to uncertainty including temporal dependence among reported COVID-NET admission counts, the impact of unmeasured site-specific factors, and the frequency and accuracy of testing for SARS-CoV-2 infection. Based on the proposed model, an estimated 6.3 million (95% uncertainty interval (UI) 5.4-7.3 million) COVID-19-associated hospital admissions occurred in the United States from September 2020 through December 2023. Between April 2020 and December 2023, model-based monthly admission rate estimates ranged from a minimum of 1 per 10,000 population (95% UI 0.7-1.2) in June of 2023 to a highest monthly level of 16 per 10,000 (95% UI 13-19) in January 2022.

Keywords: Bayesian modeling; COVID‐19; Population‐based surviellance; hospitalization; sentinel surviellance.

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Conflict of interest statement

The findings and conclusions in this manuscript are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention (CDC).

Meek J declares CDC funding for Connecticut Emerging Infections Program, Ryan PA declares salary support from an Emerging Infections program cooperative agreement with CDC, Reeg L declares funding from a CSTE/CDC federal grant, and Resini B declares receipt of honoraria from Merck for editorial board membership.

Figures

FIGURE 1
FIGURE 1
Estimated US COVID‐19‐associated hospital admission rates by month from April 2020 through December 2023 and monthly hospital admission rates reported in the National Hospital Safety Network (NHSN).
FIGURE 2
FIGURE 2
Estimated US COVID‐19‐associated hospital admission rates by month and age class from April 2020 through December 2023.
FIGURE 3
FIGURE 3
Estimated COVID‐19‐associated hospital admission rates by month and age class1 from April 2020 through December 2023 and monthly age class specific hospital admission rates reported in the National Hospital Safety Network (NHSN).
FIGURE 4
FIGURE 4
Estimated COVID‐19‐associated hospital admission rates by month from September 2020 through December 2023 and cumulative hospital admissions reported in the National Hospital Safety Network (NHSN).
FIGURE 5
FIGURE 5
Estimated COVID‐19‐associated hospital admission rates by month from April 2020 through December 2023 after adjustment for detection uncertainly only and monthly admission rates reported in the National Hospital Safety Network (NHSN).
See this image and copyright information in PMC

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