A Stressful Tour of the COVID-19 Issue of the Harvard Data Science Review

Speaker: Xiao-Li Meng (Harvard University)

Occasion: Decision Making in Health and Medical Care: Modeling and Optimization

Date: May 18, 2021: COVID-19 and Infectious Diseases

Abstract: The year-long special issue of the Harvard Data Science Review provides a glimpse into the massive stress tests COVID-19 has created for many ecosystems in our global communities. This talk samples the challenges and opportunities generated by such tests that can directly affect our ability to make hard and quality decisions in medical and health care. Questions addressed include:

Did individual acceptance of the use of personal health data for public benefit change during COVID-19? (Frederic Gerdon, Helen Nissenbaum, Ruben L. Bach, Frauke Kreuter, and Stefan Zins)
Did COVID-19 change the level of public support to health care reform in the United States? (John Sides, Chris Tausanovitch, and Lynn Vavreck)
How can we ensure responsible data science and AI innovations for combating COVID-19 and pandemics in general? (David Leslie)
How reliable are our estimates of case fatality rates (and other rates) for COVID-19? (Anastasios Nikolas Angelopoulos, Reese Pathak, Rohit Varma, and Michael I. Jordan)

The audience is also invited to examine if these studies themselves pass the stress test in the sense of maintaining high standards under stringent time constraints.