Math Flips the Perspective in Contact Tracing
Contact tracing has emerged as an important technique in controlling COVID-19. Its test-trace-isolate-support paradigm focuses on identifying potentially contagious individuals and isolating them.
This talk introduces an alternative and complementary approach which has just become achievable with present technology: for each positive case, do not only notify their direct contacts, but inform thousands of people of how far away they are from the positive case, as measured in network-theoretic distance in their relationship network (not geographical distance). This approach brings a new tool to bear on COVID-19, analogous to a weather satellite providing early warning of incoming hurricanes. It empowers individuals to observe the spread and directly avoid infection (a natural selfish instinct), reducing reliance on altruism. This could solve the behavior coordination problem which has hampered most other interventions to date.
The speaker is a math professor whose ordinary research focus is in network theory, probability, and algorithms. This talk will be different in nature from a traditional math research seminar, and will be accessible to non-math-researchers. It will also share some of the experience of starting from several mathematical research insights (network theory and basic Fourier Analysis) to implement and deploy a practical system at scale: the NOVID app.
Please join us for a post-seminar discussion from 1:30 to 2:30
This event will not be recorded.