论文标题
在大学环境中模拟Covid-19
Simulating COVID-19 in a University Environment
论文作者
论文摘要
住宅学院和大学在共同19-19大流行期间提供面对面指导方面面临着独特的挑战。目前,管理员面临有关是否在大流行期间开放的决定,以及保护学生,教职员工和教职员工可能需要进行哪些正常操作的修改。但是,关于哪些措施可能最有效的措施以及现有干预措施是否包含爆发在校园中的传播,几乎没有信息。我们开发了一个全尺寸的基于随机剂的模型,以确定在大流行期间是否可以安全地继续进行面对面的指导,并评估各种干预措施的必要性。仿真结果表明,大规模的随机测试,接触追踪和隔离是成功包含校园暴发策略的重要组成部分。高测试特异性对于保持隔离人群的规模至关重要。在线移动最大的课程对于控制疫情的大小和隔离的学生人数也至关重要。增加的住宅暴露会显着影响爆发的规模,但是控制学生之间的非住宅社会接触可能更为重要。最后,即使在受控疫情中,也必须高隔离率,这意味着明显的缺勤,这表明需要计划远程隔离学生的教学。
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.