Residential colleges and universities face unique challenges in providing in-person instruction during the COVID-19 pandemic. Administrators are currently faced with decisions about whether to open during the pandemic and what modifications of their normal operations might be necessary to protect students, faculty and staff. There is little information, however, on what measures are likely to be most effective and whether existing interventions could contain the spread of an outbreak on campus. We develop a full-scale stochastic agent-based model to determine whether in-person instruction could safely continue during the pandemic and evaluate the necessity of various interventions. Simulation results indicate that large scale randomized testing, contact-tracing, and quarantining are important components of a successful strategy for containing campus outbreaks. High test specificity is critical for keeping the size of the quarantine population manageable. Moving the largest classes online is also crucial for controlling both the size of outbreaks and the number of students in quarantine. Increased residential exposure can significantly impact the size of an outbreak, but it is likely more important to control non-residential social exposure among students. Finally, necessarily high quarantine rates even in controlled outbreaks imply significant absenteeism, indicating a need to plan for remote instruction of quarantined students.

在 COVID-19 大流行期间，住宿学院和大学在提供面对面教学方面面临着独特的挑战。管理人员目前面临着关于是否在大流行期间开放以及可能需要对其正常运营进行哪些修改以保护学生、教职员工的决定。然而，关于哪些措施可能最有效以及现有的干预措施是否可以遏制疫情在校园内的蔓延，目前还没有什么信息。我们开发了一个全面的基于随机代理的模型，以确定在大流行期间是否可以安全地继续进行现场教学，并评估各种干预措施的必要性。模拟结果表明，大规模随机检测、接触者追踪和隔离是成功遏制校园疫情爆发策略的重要组成部分。高检测特异性对于保持隔离人群规模的可控至关重要。将最大的课程转移到网上对于控制疫情规模和被隔离的学生数量也至关重要。住宅暴露的增加可能会显着影响疫情的规模，但控制学生的非住宅社交暴露可能更为重要。最后，即使在疫情受控的情况下，隔离率也必然很高，这意味着缺勤率很高，这表明需要为被隔离的学生制定远程教学计划。