Are college campuses superspreaders? A data-driven modeling study

Abstract

The COVID-19 pandemic continues to present enormous challenges for colleges and universities and strategies for save reopening remain a topic of ongoing debate. Many institutions that reopened cautiously in the fall experienced a massive wave of infections and colleges were soon declared as the new hotspots of the pandemic. However, the precise effects of college outbreaks on their immediate neighborhood remain largely unknown. Here we show that the first two weeks of instruction present a high-risk period for campus outbreaks and that these outbreaks tend to spread into the neighboring communities. By integrating a classical mathematical epidemiology model and Bayesian learning, we learned the dynamic reproduction number for 30 colleges from their daily case reports. Of these 30 institutions, 14 displayed a spike of infections within the first two weeks of class, with peak seven-day incidences well above 1,000 per 100,000, an order of magnitude larger than the nation-wide peaks of 70 and 150 during the first and second waves of the pandemic. While most colleges were able to rapidly reduce the number of new infections, many failed to control the spread of the virus beyond their own campus: Within only two weeks, 17 campus outbreaks translated directly into peaks of infection within their home counties. These findings suggests that college campuses are at risk to develop an extreme incidence of COVID-19 and become superspreaders for neighboring communities. We anticipate that tight test-trace-quarantine strategies, flexible transition to online instruction, and–most importantly–compliance with local regulations will be critical to ensure a safe campus reopening after the winter break.

Keywords:

• Coronavirus
• COVID-19
• machine learning
• epidemiology
• SEIR model

Acknowledgments

Hannah Lu, Cortney Weintz, Joseph Pace, Dhiraj Indana contributed equally to this manuscript. This work was a class project of the course ME233 Data-driven modeling of COVID-19, and we acknowledge the support by Amelie Schafer, Oguz Tikenogullari, and Mathias Peirlinck. It was supported by a DAAD Fellowship to Kevin Linka, by a Stanford Bio-X IIP seed grant to Ellen Kuhl, and by the Stanford School of Engineering COVID-19 Research and Assistance Fund.

Disclosure statement

The authors declare no conflict of interest.