Laura A. Hatfield presents "Adaptive metrics for an evolving pandemic"

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. I this talk, I will highlight recent work that makes two key contributions to address these weaknesses of risk metrics. I 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 policymakers to optimize metrics for specific preferences and interventions. Second, I will present a novel method to update risk thresholds in real-time. Our proposed adaptive metrics have a unique advantage in a rapidly evolving pandemic context. In this talk, I also connect these ideas to new causal identification strategies in difference-in-differences.