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Extrapolating Sentinel Surveillance Information to Estimate National COVID Hospital Admission Rates: A Bayesian
Owen Devine1, Huong Pham2, Betsy Gunnels3
1Eagle Global Scientific, LLC, Atlanta, Georgia, USA.
Influenza and Other Respiratory Viruses
|October 23, 2024
Summary
The COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) monitored over 6 million US hospital admissions due to SARS-CoV-2 infection. This surveillance system used Bayesian modeling to estimate national hospitalization rates and their uncertainties.
Area of Science:
- Epidemiology
- Public Health Surveillance
- Biostatistics
Background:
- The COVID-19 pandemic necessitated robust surveillance systems to track severe disease burden.
- The COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) was established to monitor SARS-CoV-2 hospitalizations across diverse US populations.
- Understanding hospitalization trends is crucial for public health response and resource allocation.
Purpose of the Study:
- To propose and validate a Bayesian modeling approach for estimating national COVID-19-associated hospital admission rates.
- To quantify uncertainty in national estimates derived from the COVID-NET surveillance data.
- To analyze temporal trends and contributing factors to COVID-19 hospitalizations.
Main Methods:
- Utilized data from the COVID-NET surveillance system, covering approximately 10% of the US population.
- Employed a Bayesian modeling framework to extrapolate site-specific hospitalization data to the national level.
- Accounted for various sources of uncertainty, including temporal dependencies, site-specific factors, and testing accuracy.
Main Results:
- An estimated 6.3 million (95% UI 5.4-7.3 million) COVID-19-associated hospital admissions occurred in the US from September 2020 to December 2023.
- Monthly hospitalization rates varied significantly, with a low of 1 per 10,000 in June 2023 and a peak of 16 per 10,000 in January 2022.
- The model successfully estimated national rates while addressing key sources of uncertainty.
Conclusions:
- The proposed Bayesian modeling approach provides reliable national estimates of COVID-19 hospitalizations using COVID-NET data.
- Surveillance data and advanced statistical methods are essential for monitoring infectious disease trends and informing public health strategies.
- Continued monitoring of COVID-19 hospitalizations is vital for understanding the pandemic's impact and guiding future interventions.
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