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Join Us Dec 10 at the COVID-19 Data Forum: Using Mobility Data To Forecast COVID-19 Cases

By November 18, 2020Blog

Thurs, December 10th, 9am PDT/12pm EDT/18:00 CEST – Register now!

Hosted by the COVID-19 Data Forum/Stanford Data Science Initiative/R Consortium

Join the R Consortium and learn about mobility data in monitoring and forecasting COVID-19. COVID-19 is the first global pandemic to occur in the age of big data. All around the world, public health officials are testing and releasing data to the public, giving ample cases for scientists to analyze and forecast in real-time.

Despite having so much data available, the data itself has been limited by simplistic metrics rather than higher dimensional patient-level data. To understand how COVID-19 works within the body and is transmitted, scientists must understand why the virus causes harm to some more than others.

Sharing this type of data brings up patient confidentiality issues, making it difficult to get this type of vital data.

The COVID-19 Data Forum, a collaboration between Stanford University and the R Consortium, will discuss the ways in which people’s mobility data holds promises and challenges in combating the spread of SARS-CoV-2, as well as how the public has behaved in response to the pandemic. This data is vital in understanding the way individual’s patterns have shifted since the pandemic, helping us to better understand where people are going and when they are getting sick. 

The event is free and open to the public.

Speakers include:

  • Chris Volinksky, PhD Associate vice-president, Big Data Research, ATT Labs.
  • Caroline Buckee Associate Professor of Epidemiology and Associate Director of the Center for Communicable Disease Dynamics at the Harvard T.H. Chan School of Public Health.
  • Christophe Fraser Professor of Pathogen Dynamics at University of Oxford and Senior Group Leader at Big Data Institute, Oxford University, UK.
  • Andrew Schoeder, PhD Vice-president Research & Analytics for Direct Relief.

Registration and more info: