Adaptive metrics for an evolving pandemic A dynamic approach to area-level COVID-19 risk designations.

ABSTRACT: Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and often lack transparency in terms of prioritization of false positive versus false negative signals. They have also struggled to maintain relevance over time due to slow and infrequent updates addressing new variants and shifts in vaccine- and infection-induced immunity. We make two contributions to address these weaknesses of risk metrics. We first present a framework to evaluate predictive accuracy based on policy targets related to severe disease and mortality, allowing for explicit preferences toward false negative versus false positive signals. This approach allows policy

SUBMITTER: Bilinski AM 

PROVIDER: S-EPMC9949193 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature