Simulating COVID-19 classroom transmission on a university campus.
Simulating COVID-19 classroom transmission on a university campus.
复制标题
DOI：
10.1073/pnas.2116165119
复制
发表时间：
2022-05-31
影响因子：
11.1
通讯作者：
中科院分区：
文献类型：
--
作者：
本文模拟了COVID-19通过课堂环境中的空气传播在大学中的传播。用于这些模拟的传播风险模型考虑了学生特定的课程安排、教室大小和占用率、通风率以及疫苗接种率和有效性。我们的模拟重现了在美国一所大型大学每周感染率中观察到的趋势。我们还评估了校园运营政策的影响。模型预测显示，将90%的课程转移到网上可以减少多达的新感染，而普遍使用口罩可以减少多达的新感染。对于全日制的面对面教学，预计高疫苗接种率将遏制传染性更强的严重急性呼吸系统综合征冠状病毒2变种的传播。我们研究了与在大学校园内举办面对面课程相关的空气传播风险，包括原始菌株和传染性更强的严重急性呼吸道综合征冠状病毒2型（SARS-CoV-2）变体。我们采用了一个封闭房间内空气传播风险模型，该模型考虑了房间属性、口罩效率和居住者的初始感染概率。此外，我们还研究了疫苗接种对病毒传播的影响。使用美国一所大型大学的2019年秋季（大流行前）和2020年秋季（混合教学）课程注册数据对所提出的模型进行了模拟评估，从而评估了面对面和混合课程之间的传播风险差异以及占用减少，戴口罩和接种疫苗的影响。模拟结果表明，如果不接种疫苗，将90%的课程转移到网上，新病例将减少17至18倍，而普遍使用口罩将使课堂互动中的新感染病例减少2.7倍。此外，结果表明，对于原始变体和使用效力大于90%的疫苗，至少有23%（64%）的学生需要在使用（不使用）口罩的情况下接种疫苗，以便在满员的情况下运营大学，同时防止由于课堂互动而导致的病例增加。对于传染性更强的变种，即使普遍使用口罩，至少93%的学生需要接种疫苗，以确保相同的条件。我们表明，该模型能够预测2021年秋季每周感染率的趋势。
This paper simulates the spread of COVID-19 at universities via airborne transmission in classroom settings. The transmission risk model used for these simulations accounts for student-specific class schedules, classroom sizes and occupancy, and ventilation rates, as well as vaccination rate and efficacy. We show the simulations reproduce trends observed in weekly infection rates at a large US university. We also evaluate the impact of campus operational policies. Model predictions show moving 90% of classes online can reduce new infections by as much as , and universal mask usage can reduce new infections by up to . For full-time in-person instruction, high vaccination rates are predicted to curb transmission even for more contagious variants of severe acute respiratory syndrome coronavirus 2. We study the airborne transmission risk associated with holding in-person classes on university campuses for the original strain and a more contagious variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We adopt a model for airborne transmission risk in an enclosed room that considers room properties, mask efficiency, and initial infection probability of the occupants. Additionally, we study the effect of vaccination on the spread of the virus. The presented model has been evaluated in simulations using fall 2019 (prepandemic) and fall 2020 (hybrid instruction) course registration data of a large US university, allowing for assessing the difference in transmission risk between in-person and hybrid programs and the impact of occupancy reduction, mask-wearing, and vaccination. The simulations indicate that without vaccination, moving 90% of the classes online leads to a 17 to 18× reduction in new cases, and universal mask usage results in an ∼2.7 to reduction in new infections through classroom interactions. Furthermore, the results indicate that for the original variant and using vaccines with efficacy greater than 90%, at least 23% (64%) of students need to be vaccinated with (without) mask usage in order to operate the university at full occupancy while preventing an increase in cases due to classroom interactions. For the more contagious variant, even with universal mask usage, at least 93% of the students need to be vaccinated to ensure the same conditions. We show that the model is able to predict trends observed in weekly infection rates for fall 2021.
登录
查看更多内容
影响因子：
7.8
作者：
Mandal, Manotosh;Jana, Soovoojeet;Kar, T. K.
通讯作者：
Kar, T. K.
影响因子：
17.1
作者：
Konda, Abhiteja;Prakash, Abhinav;Guha, Supratik
通讯作者：
Guha, Supratik
影响因子：
2.6
作者：
Barton, Daniel C.
通讯作者：
Barton, Daniel C.
DOI：
10.1017/flo.2021.10
发表时间：
2021-10-04
期刊：
FLOW
影响因子：
--
作者：
Bazant, Martin Z.;Kodio, Ousmane;Bush, John W. M.
通讯作者：
Bush, John W. M.
影响因子：
11.8
作者：
Buonanno, G.;Stabile, L.;Morawska, L.
通讯作者：
Morawska, L.