Title: Fighting COVID-19 at Cornell University

Recorded Session on YouTube

Presentation Slides

Abstract: Universities around the world faced a challenging decision during the summer of 2020: whether to reopen for in-person instruction despite the pandemic and how to protect campus populations if they did. Operations research and data science were a fundamental part of these decisions at Cornell University in the USA. First, models developed by Cornell's COVID-19 Mathematical Modeling Team were used to design the testing interventions that are a cornerstone of Cornell’s COVID-19 control strategy: targeted asymptomatic screening that tests all undergraduates twice per week and an adaptive testing program that goes beyond traditional contact tracing to test the full social circle of positive cases. Second, these same models were the basis for Cornell's decision to reopen for a fall semester with in-person instruction. They showed that reopening with aggressive mandatory testing was surprisingly less risky than virtual instruction. Data suggested that thousands of students would return to the area whether in-person instruction was offered or not, and a weaker ability to enforce mandatory testing for these students risked being unable to control clusters in that population. Reopening with asymptomatic screening was successful, with only 0.5% of students, staff and faculty infected over the semester. This talk will share insights from this experience and explain practical tools that supported this work.

About AI4PAN Artificial Intelligence for Pandemics Seminar Series centred at UQ

Welcome to AI4PAN, the Artificial Intelligence for Pandemics group centered at The University of Queensland (UQ). The group's focus is the application of data science, machine learning, statistical learning, applied mathematics, computation, and other "artificial intelligence" techniques for managing pandemics both at the epidemic and clinical level.