Mobility data to monitor and forecast the COVID-19 pandemic

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

Date: Thursday Dec 10th, 12pm (noon) Eastern Time (UTC-4)
Registration and more info:

Description: COVID-19 spreads by close contact between individuals, and around the world control efforts have focused on reducing human-to-human contact by “social distancing” and “stay-at-home” policies. Despite the central role of human movement and contact in the pandemic, it remains extremely challenging to infer the past route of virus spread, forecast future transmission pathways, or monitor the reduction in person-to-person contacts in response to policies. Large-scale human mobility data has emerged as a novel data source to approximate human contact. This data is now being produced by telecommunications companies, smartphone manufacturers, social media platforms, transit agencies, and more, and analyzed by researchers across domains. The COVID-19 Data Forum, a collaboration between Stanford University and the R Consortium, is hosting the event “Using mobility data to monitor and forecast the COVID-19 pandemic” to bring together experts generating and using mobility data. This public webinar and discussion will focus on the key challenges and opportunities for mobility data, including data access, privacy concerns, biases, similarities and differences between sources, and the utility for modeling and public health decision making. The event will be open to the public, and is part of a continuing series focusing on data-related aspects of the scientific response to the pandemic. 

Speakers include:

  • Caroline Buckee, Associate Professor of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health

  • Andrew Schroeder, Vice President of Research and Analytics, Direct Relief

  • Christophe Fraser, Professor and Senior Group Leader in Pathogen Dynamics, Big Data Institute, Oxford University

  • Moderated by Chris Volinsky, Associate Vice President of Big Data Research, AT&T Labs

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