Fig. 1.

COVID-19 infection rates as a function of the number of visits to businesses and public places in the city of Chicago based on a simulation approach using mobile phone tracking data as described in Chang et al. (9). The model predicts that infections rise nonlinearly with the number of visits to businesses and public places. This highlights the trade-offs between infections and activity restrictions. For example, reducing visits by 50% prevents ∼1 million infections. Shading indicates the 95% confidence interval. Image credit: Serina Chang (Stanford University, Stanford, CA), Emma Pierson (Stanford University, Stanford, CA), Pang Wei Koh (Stanford University, Stanford, CA), Jaline Gerardin (Northwestern University, Chicago, IL), Beth Redbird (Northwestern University, Evanston, IL), David Grusky (Stanford University, Stanford, CA), and Jure Leskovec (Stanford University, Stanford, CA).