Delphi’s COVIDcast Project: Lessons from Building a Digital Ecosystem for Tracking and Forecasting the Pandemic
When and Where
Speakers
Description
In March 2020, the Delphi group at CMU launched an effort called COVIDcast, which has many parts: 1. unique relationships with partners in tech/healthcare granting us access to data on pandemic activity; 2. infrastructure to build real-time, geographically-detailed COVID-19 indicators from this data; 3. a historical database of all indicators, including revision tracking; 4. a public API serving new indicators daily (with R and Python client support); 5. interactive graphics to display our indicators; 6. forecasting and modeling work building on the indicators. This talk gives a high-level summary, with discussion of some lessons learned.
About Ryan Tibshirani
Professor Tibshirani is jointly appointed in the Departments of Statistics and Machine Learning at Carnegie Mellon University. He joined the Statistics faculty at Carnegie Mellon University in 2011, and I joined the Machine Learning faculty in 2013. I did my Ph.D. in Statistics at Stanford University in 2011. My thesis advisor was Jonathan Taylor. Before that, I did my B.S. in Mathematics at Stanford University in 2007.
Prof. Tibshirani’s research interests lie broadly in statistics, machine learning, and optimization. More specifically, high-dimensional statistics, nonparametric estimation, distribution-free inference, continuous optimization, and numerical analysis. His main applied focus at this time is on tracking and forecasting epidemics (previously focused on seasonal flu, and now COVID-19).