Monitoring carbon dioxide to quantify the risk of indoor airborne transmission of COVID-19
Monitoring carbon dioxide to quantify the risk of indoor airborne transmission of COVID-19
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DOI：
10.1017/flo.2021.10
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发表时间：
2021-10-04
期刊：
影响因子：
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通讯作者：
Bush, John W. M.
中科院分区：
文献类型：
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作者：
Bazant, Martin Z.;Kodio, Ousmane;Bush, John W. M.
减轻COVID-19室内空气传播的新指南规定了与受感染个体在共享空间中花费的时间限制（Bazant & Bush，Proceedings of the National Academy of Sciences of the United States of America，第118卷，第17期，2021年，e2018995118）。在这里，我们根据室内空间的占用时间和平均呼出二氧化碳（CO2）浓度重新表述了该安全指南，从而能够在呼吸道疾病空气传播的风险评估中使用CO2监测仪。而CO2浓度与空气传播的病原体浓度有关（Rudnick和米尔顿，室内空气，第13卷，第3期，2003，第13 - 14页）。237-245），这里制定的指南解释了影响其演变的不同物理过程，例如通过声音活动增强病原体产生和通过使用面罩、过滤、沉淀和灭活去除病原体。关键是，传播风险取决于总感染剂量，因此必然取决于病原体浓度和暴露时间。传播风险还受到人群中易感、感染和免疫人群比例的调节，这些比例随着大流行病的发展而变化。开发了一个数学模型，可以从实时CO2测量中预测空气传播风险。实施我们的指导方针的说明性例子是使用二氧化碳监测数据在大学教室和办公空间。
A new guideline for mitigating indoor airborne transmission of COVID-19 prescribes a limit on the time spent in a shared space with an infected individual (Bazant & Bush, Proceedings of the National Academy of Sciences of the United States of America, vol. 118, issue 17, 2021, e2018995118). Here, we rephrase this safety guideline in terms of occupancy time and mean exhaled carbon dioxide (CO2) concentration in an indoor space, thereby enabling the use of CO2 monitors in the risk assessment of airborne transmission of respiratory diseases. While CO2 concentration is related to airborne pathogen concentration (Rudnick & Milton, Indoor Air, vol. 13, issue 3, 2003, pp. 237-245), the guideline developed here accounts for the different physical processes affecting their evolution, such as enhanced pathogen production from vocal activity and pathogen removal via face-mask use, filtration, sedimentation and deactivation. Critically, transmission risk depends on the total infectious dose, so necessarily depends on both the pathogen concentration and exposure time. The transmission risk is also modulated by the fractions of susceptible, infected and immune people within a population, which evolve as the pandemic runs its course. A mathematical model is developed that enables a prediction of airborne transmission risk from real-time CO2 measurements. Illustrative examples of implementing our guideline are presented using data from CO2 monitoring in university classrooms and office spaces.