Talk today -- Choices give meaning to uncertainty.
I had the privilege to give a talk today for Foundations of Biomedical Data Science seminar series hosted by the Innovation Lab at the University of Virginia.
The irresistible prompt was, “Lectures… can be on any topic you prefer that is related to the secondary consequences of the COVID-19 pandemic or the data science challenges associated with it.”
I decided to run with it, to speak personally on doing science at the very start of the COVID-19 pandemic and learning to live through it as a person on immunosuppressive therapy for MS. I told some stories that I’m still learning to understand, and wasn’t bashful about putting a few normative thoughts out there about doing good science during trying times. I’ll probably take a lot more passes over this in the years to come, but if you’re interested in a rough, inside look at what January to March 2020 was like and some of the challenges of knowing too much, give it a watch on YouTube. My only regret was Zoom decided to cover all the titles with a gray bar for some reason.
Choices Give Meaning to Uncertainty: Stories from Pandemic Emergence, Policy Advising, and (very) Personalized Medicine.
So much of our experience of the COVID-19 pandemic was dominated by uncertainty, but it didn’t have to be. By working backwards from the decisions, it is often possible to arrive at clear actions despite uncertainty. When clarity remains elusive, making the effort to understand why leads to better scientific questions and often reveals that debates about data are masking disagreements about values. Good scientists must grapple with the feedbacks between decisions, data, analysis, and uncertainty. In this talk, Mike Famulare will discuss these issues in the context of his work on pandemic emergence, policy advising, and as a person living with multiple sclerosis forced to navigate trade-offs in COVID prevention and MS care.
Slides (without the gray bar blocking the titles) are here.