Data science is often described as the process of turning data into action. The novel coronavirus pandemic has attached an urgent reality to that simple directive. It has brought the complexities and uncertainties of data-driven decision making into public view on an unprecedented scale.
The pandemic has dramatically and universally impacted our everyday lives. We have therefore invested time to scan data dashboards, learn about model-based forecasting, and understand the importance of randomized comparisons of new treatments.
Still, we have questions. What percentage of us is or has been infected? Why do the model projections change so frequently, and which ones should I believe? Do we really need randomized trials before rolling out promising new treatments? What kinds of data will 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 brings to Brown experts who are directly engaged in COVID-related data-driven research activities. Instead of the usual long-format lecture, each seminar will feature a 20 to 30–minute presentation by the speaker, followed by a moderated question and answer period. All seminars are virtual, and open to the public.
- Elizabeth Ogburn, Johns Hopkins: A collaboration platform to facilitate aggregating evidence across COVID-19 RCTs Watch video
- Nicholas Jewell, UC Berkeley and London School of Hygiene and Tropical Medicine Watch Video
- Natalie Dean, University of Florida: COVID-19 Vaccine Evaluation Watch Video
- Elizabeth Stuart, Johns Hopkins: Policy Evaluation during the Pandemic Watch Video
- Evan Ray, University of Massachusetts: The Covid-19 Forecast Hub Watch Video
- Thomas Trikalinos, Brown University: Modeling the SARS-COV-2 Pandemic in Rhode Island Watch Video
- Xihong Lin, Harvard University: Learning from COVID-19 Data in Wuhan, USA, and the World on Transmission, Health Outcomes, and Interventions Watch Video