A team of researchers has created a computer model that accurately predicted the spread of COVID-19 in 10 major cities this spring by analyzing three factors that drive infection risk: where people go in the course of a day, how long they linger and how many other people are visiting the same place at the same time.
“We built a computer model to analyze how people of different demographic backgrounds, and from different neighborhoods, visit different types of places that are more or less crowded. Based on all of this, we could predict the likelihood of new infections occurring at any given place or time,” said Jure Leskovec, the Stanford computer scientist who led the effort, which involved researchers from Northwestern University.
