# TaxProf Blog

Paul L. Caron
Dean

Friday, May 15, 2020

### Modeling The Spread Of COVID-19 At UCLA

Daily Bruin, Modeling the Spread of COVID-19 in UCLA Classrooms:

Key takeaways

• According to our model of the undergraduate student network, each UCLA student shares a class with 228 other students on average.
• Our simulation shows that with an R0 value of 5.7, 94% of UCLA undergraduates could be infected by the end of fall quarter, and with an R0 value of 2.0, 8% of UCLA undergraduates could be infected.

Introduction
In the middle of a global pandemic, uncertainty has become the new normal. Many states across the country are now beginning to lift restrictions, but colleges must weigh the difficult decision of how to keep students and staff safe while still providing a quality education. While UCLA has already decided to move summer sessions A and C online, the fate of fall quarter is still up in the air. Crowded lecture halls and dorm rooms make it nearly impossible for students to practice social distancing without a disruption to normal college life. As UCLA grapples with whether to welcome students back to campus in the fall, The Stack examines how quickly COVID-19 could spread through the undergraduate student body. Inspired by professor Kim Weeden’s model of course enrollment networks at Cornell University, we created our own model of how connected UCLA students are based on the classes they enroll in. We also thank Professor Mason Porter and Professor Stephanie Wang from the UCLA Math department for providing guidance on modeling the student networks and for their constructive comments in the development of this piece. ...

In our model network, students had an average of 228 connections. We ran the simulation 100 times from week 0 to finals week with an R0 value of 5.7, and found that on average, 94% of students were infected by the end of fall quarter. The peak of new cases occurred at week 6 with over 11,000 new cases. With a smaller R0 of 2.0, we found that 8% of students were infected by the end of fall quarter. We also calculated the average number of infections over 100 runs for several different values of R0. The following chart shows the number of people infected on average through the 11 weeks, for varying values of R0:

For complete TaxProf Blog coverage of the coronavirus, see here.