Laura Hatfield (HMS), "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. In 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.
The Applied Statistics Workshop (Gov 3009) meets all academic year, Wednesdays, 12pm-1:30pm, in CGIS K354. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers.
More information is available at the Gov 3009 website: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
All interested Harvard affiliates are invited to attend.
LOCATION:CGIS Knafel Building, room K354 or Online via Zoom STATUS:CONFIRMED DTSTART:20230301T170000Z DTEND:20230301T183000Z END:VEVENT END:VCALENDAR