Data science is often described as the process of turning data into action. The novel coronavirus pandemic attached an urgent reality to that simple directive, bringing the complexities and uncertainties of data-driven decision making into public view on an unprecedented scale.
The pandemic dramatically and universally impacted our everyday lives, and we invested time to scan data dashboards, learn about model-based forecasting, and understand the importance of randomized comparisons of new treatments.
Early in the pandemic, there was a great deal of uncertainty. What percentage of us had been infected? Why did model projections change so frequently, and which ones should we believe? Do we really need randomized trials before rolling out promising new treatments? What kinds of data would tell us when we can reopen the economy? And perhaps most importantly, how can I decide which information to trust in this avalanche of data?
This seminar series was an early foray into the virtual format that is now so familiar. We brought to Brown experts who were directly engaged in COVID-related data-driven research activities. We have learned much since then, but these talks address many basic principles of pandemic research and policy that remain highly relevant.
- Elizabeth Ogburn, Johns Hopkins: A collaboration platform to facilitate aggregating evidence across COVID-19 RCTs
- Nicholas Jewell, UC Berkeley and London School of Hygiene and Tropical Medicine: The Exponential Power of Today (and Yesterday?)
- Natalie Dean, University of Florida: COVID-19 Vaccine Evaluation
- Elizabeth Stuart, Johns Hopkins: Policy Evaluation during the Pandemic
- Evan Ray, University of Massachusetts: The Covid-19 Forecast Hub
- Thomas Trikalinos, Brown University: Modeling the SARS-COV-2 Pandemic in Rhode Island
- Xihong Lin, Harvard University: Learning from COVID-19 Data in Wuhan, USA, and the World on Transmission, Health Outcomes, and Interventions