A comparative analysis of COVID-19 seroprevalence rates, observed infection rates, and infection-related mortality DOI Creative Commons
Eric W. Ford, Kunal N. Patel,

Holly Ann Baus

et al.

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 13

Published: Jan. 28, 2025

The COVID-19 pandemic highlighted the need for data-driven decision making in managing public health crises. This study aims to extend previous research by incorporating infection-related mortality (IRM) evaluate discrepancies between seroprevalence data and infection rates reported Centers Disease Control Prevention (CDC), assess implications policy. We conducted a comparative analysis of collected as part an NIH CDC-reported across ten U.S. regions, focusing on their correlation with IRM calculations. includes revision prior estimates using updated rates. Correlations were calculated statistical relevance assessed. Findings indicate that is approximately 2.7 times more prevalent than what CDC suggest. Utilizing lower calculate leads significant overestimation factor 2.7. When both are combined, increases 3.79. highlights importance integrating multiple dimensions accurately understand manage emergencies. results suggest agencies should enhance capacity collecting analyzing regularly, given its stronger other estimates. approach will better inform policy decisions direct effective interventions.

Language: Английский

A comparative analysis of COVID-19 seroprevalence rates, observed infection rates, and infection-related mortality DOI Creative Commons
Eric W. Ford, Kunal N. Patel,

Holly Ann Baus

et al.

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 13

Published: Jan. 28, 2025

The COVID-19 pandemic highlighted the need for data-driven decision making in managing public health crises. This study aims to extend previous research by incorporating infection-related mortality (IRM) evaluate discrepancies between seroprevalence data and infection rates reported Centers Disease Control Prevention (CDC), assess implications policy. We conducted a comparative analysis of collected as part an NIH CDC-reported across ten U.S. regions, focusing on their correlation with IRM calculations. includes revision prior estimates using updated rates. Correlations were calculated statistical relevance assessed. Findings indicate that is approximately 2.7 times more prevalent than what CDC suggest. Utilizing lower calculate leads significant overestimation factor 2.7. When both are combined, increases 3.79. highlights importance integrating multiple dimensions accurately understand manage emergencies. results suggest agencies should enhance capacity collecting analyzing regularly, given its stronger other estimates. approach will better inform policy decisions direct effective interventions.

Language: Английский

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