Ben Shaya and Michael Cohn:
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MicroCOVID: using limited data to create a practical model of COVID risk
Even after extensive global research on the physical and biological dynamics of SARS-CoV-2 spread, much is still unknown about how to estimate COVID risk. Nevertheless, every person on the planet must make decisions that factor this risk into their everyday lives. We describe a simplified model for predicting COVID risk, along with a user interface that helps lay users apply it to real-life situations. The model has been in use for a year and currently has >2000 users per day. We'll share stories about its benefits, limitations, and both intended and unintended consequences.
Benjamin Shaya graduated from MIT with an Electrical Engineering and Computer Science degree in 2014. He worked on devices to detect sleep apnea in infants. He moved to Google in 2016 where he built the sound output engine for Google Home, In 2019 he had a brief stint at an electronic health record startup, but is now back at Google. At microCOVID, Benjamin leads research and modelling. He is equal parts confused and delighted to wind up giving medical advice to 2000 people per day.
Dr Michael Cohn is a social psychologist and user experience researcher. If you've used Google Maps you may have seen some of his work -- or rather, not seen it, since his job there was to make sure that new features didn't get in the way of people's everyday direction and safety needs. With microCOVID, he helps the team translate the messy complexities of virus transmission into terms that help real-life users make choices about the things that matter to them. Michael holds a PhD in psychology from the University of Michigan and is currently Deputy Director of Research at VoteTripling.org.