Simulating COVID-19 in a university environment

Area of Science:

• Epidemiology
• Public Health
• Higher Education

Background:

• Universities face challenges in maintaining in-person instruction during COVID-19.
• Limited data exists on effective interventions to control campus outbreaks.

Purpose of the Study:

• To model the safety of in-person instruction during the pandemic.
• To evaluate the necessity and impact of various interventions for outbreak containment on campuses.

Main Methods:

• Development of a full-scale stochastic agent-based model.
• Simulation of different intervention strategies for controlling campus outbreaks.

Main Results:

• Large-scale randomized testing, contact tracing, and quarantine are vital for outbreak containment.
• High test specificity is critical for managing quarantine population size.
• Moving large classes online significantly controls outbreak size and quarantine numbers.
• Controlling non-residential social exposure is more critical than residential exposure.
• High quarantine rates necessitate planning for remote instruction.

Conclusions:

• A multi-faceted approach combining testing, tracing, quarantine, and modified class structures is necessary for safe in-person learning.
• Strategic planning for remote instruction is essential due to unavoidable absenteeism from quarantine.