I’m a data scientist at the University of San Francisco and teach courses online in machine learning for fast.ai. In late March, I decided to use public mask-wearing as a case study to show my students how to combine and analyze diverse types of data and evidence.
Much to my surprise, I discovered that the evidence for wearing masks in public was very strong. It appeared that universal mask-wearing could be one of the most important tools in tackling the spread of COVID-19. Yet the people around me weren’t wearing masks and health organizations in the U.S. weren’t recommending their use.
I, along with 18 other experts from a variety of disciplines, conducted a review of the research on public mask-wearing as a tool to slow the spread SARS-CoV-2. We published a preprint of our paper on April 12 and it is now awaiting peer review at the Proceedings of the National Academy of Sciences. Continue reading.