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An Ecosystem for Tracking and Forecasting the Pandemic
Presentation Slides: https://cmu-delphi.github.io/covidcast/talks/copss-niss/talk.html#
About this Webinar Series
The COPSS-NISS COVID-19 Data Science webinar series is co-organized by the Committee of the Presidents of Statistical Societies (COPSS) and its five charter member societies (ASA, ENAR, IMS, SSC, and WNAR), as well as NISS. This bi-weekly seminar features the latest research that is positioned on the cusp of new understanding and analysis of COVID-19 pandemic data, and promotes data-driven research and decision making to combat COVID-19. Find out more about this series and view all the previous sessions on the Webinar Series page.
We all know that data is the foundation on which statistical modeling rests. To provide a better foundation for COVID-19 tracking and forecasting, the Delphi group launched an effort called COVIDcast, which has many parts:
- Unique relationships with partners in tech and healthcare granting us access to real-time data on pandemic activity.
- Code and infrastructure to build COVID-19 indicators, continuously-updated and geographically comprehensive.
- A historical database of all indicators, including revision tracking, currently with hundreds of millions of observations.
- A public API serving new indicators daily (and R and Python packages for client support).
- Interactive maps and graphics to display our indicators.
- Forecasting and modeling work building on the indicators.
In this talk, we'll summarize the various parts, and highlight some interesting findings so far. We'll also describe ways you can get involved yourself, access the data we've collected, and leverage the tools we've built.
Roni Rosenfeld (Carnegie Mellon University)
Professor and Head, Machine Learning Department
School of Computer Science
Ryan Tibshirani (Carnegie Mellon University)
Department of Statistics and Machine Learning Department
Rob Tibshirani (Stanford University)
Professor of Biomedical Data Science, and Statistics
Xihong Lin (Chair) (IMS), Harvard University
Karen Bandeen-Roche (NISS), Johns Hopkins University
Chris Barker (ASA), Statistical Planning and Analysis Services, Inc
Gary Chan (WNAR), University of Washington
Rob Deardon (SSC), University of Calgary
Natalie Dean (COPSS), University of Florida
Debashree Ray (COPSS), Johns Hopkins University
Jie Peng (WNAR), University of California at Davis
Nathaniel Stevens (SSC), University of Waterloo
Elizabeth Stuart (ENAR), Johns Hopkins University
Ryan Tibshirani (IMS), Carnegie Mellon University
Lily Wang (ASA), Iowa State University
Lingzhou Xue (NISS), Pennsylvania State University
Lili Zhao (ENAR), University of Michigan
Glenn Johnson (Web Communications), NISS