What can machine learning offer COVID-19?

Wednesday, June 17, 2020
7:00 PM – 9:00

Zoom LIVE-streaming: (register on our meetup-event for link)


By Rajiv Bhatia, MD, MPH, Stanford University


7:00 Announcements and Presentation
Use “Q&A” in Zoom for questions
Use “Chat” or “Raise hand” for technical issues



Current Situation

●A novel virus, Covid-19, known to cause severe pneumonia and death, but with highly uncertain information on transmission behavior, human susceptibility, and immunity.
●Protective human behavioral and governmental responses driven by uncertainty and threat resulting in unprecedented economic disruption and unemployment.
●Secondary social and health harms including anxiety, delayed medical care, domestic violence, etc.
●Long term risks of maladaptive human behaviors, including economic depression, worsening social division and inequality.

Machine learning might ….

●Better understand or predict viral behavior both naturally and under different scenarios of control (e.g. disease modeling).
●Create an early warning indicator for hospital demand.
●Estimate real-time, location, specific individualized risks of Covid-19 disease at the individual, community, and business levels in real time. For example, what’s my risk of going to a restaurant in San Francisco tonight or taking a vacation to New York City.
●Sense where knowledge is converging and where uncertainty remains.
●Identify unexpected consequences, e.g. 3rd and 4th order effects, blind spots.

Speaker Bio:

Dr. Rajiv Bhatia is a physician and internationally recognized health systems innovator. From 1998 through 2013, he led San Francisco’s pioneering work on health impact assessment (HIA), community health indicators, and open civic data — all strategies to generate actionable civic intelligence on the health externalities of social and economic systems beyond healthcare. He currently works as a practicing primary care physician and as a consultant to civil society organizations, healthcare systems, and governments designing and implementing informatics practices to address the community, economic, and environmental roots of health.