Jure Leskovec, Stanford University
In this talk I will demonstrate how fine-grained epidemiological modeling of the spread of Coronavirus – predicting who gets infected at which locations – can aid the development of policy responses that account for heterogeneous risks of different locations as well as the disparities in infections among different demographic groups. We use U.S. cell phone data to capture the hourly movements of millions of people and model the spread of Coronavirusfrom among a population of nearly 100 million people in 10 of the largest U.S. metropolitan areas. We show that even a relatively simple epidemiological model can accurately capture the case trajectory despite dramatic changes in population behavior due to the virus. We also estimate the impacts of fine-grained reopening plans: we predict that a small minority of superspreader locations account for a large majority of infections, and that reopening some locations (like restaurants) pose especially large risks. We also explain why infection rates among disadvantaged racial and socioeconomic groups are higher. Overall, our model supports fine-grained analyses that can inform more effective and equitable policy responses to the Coronavirus.