This event is part of the AI for Social Impact Seminar Series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.
“Mobility Networks for Modeling the Spread of COVID-19: Explaining Infection Rates and Informing Reopening Strategies”
In this talk, Dr. Leskovec 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. He will demonstrate the use of U.S. cell phone data to capture the hourly movements of millions of people and model the spread of Coronavirus from among a population of nearly 100 million people in 10 of the largest U.S. metropolitan areas. Dr. Leskovic will show that even a relatively simple epidemiological model can accurately capture the case trajectory despite dramatic changes in population behavior due to the virus. He also estimates the impacts of fine-grained reopening plans: he predicts 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. He also explains why infection rates among disadvantaged racial and socioeconomic groups are higher. Overall, his model supports fine-grained analyses that can inform more effective and equitable policy responses to the Coronavirus.
About the Speaker
Jure Leskovec is an associate professor of computer science at Stanford University, the Chief Scientist at Pinterest, and an Investigator at the Chan Zuckerberg Biohub. He co-founded a machine learning startup, Kosei, which was later acquired by Pinterest. Leskovec's research area is machine learning and data science for complex, richly-labeled relational structures, graphs, and networks for systems at all scales, from interactions of proteins in a cell to interactions between humans in a society. Applications include commonsense reasoning, recommender systems, social network analysis, computational social science, and computational biology with an emphasis on drug discovery. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper and test of time awards. It has also been featured in popular press outlets such as the New York Times and the Wall Street Journal. Leskovec received his bachelor's degree in computer science from the University of Ljubljana, Slovenia, Ph.D. in machine learning from Carnegie Mellon University, and postdoctoral training at Cornell University.