Guo Yong, Qian Hua, Sun Zhiwei, Cao Jianping, Liu Fei, Luo Xibei, Ling Ruijie, Weschler Louise B, Mo Jinhan, Zhang Yinping
Department of Building Science, Tsinghua University, Beijing, China.
Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Beijing, China.
Sustain Cities Soc. 2021 Apr;67:102719. doi: 10.1016/j.scs.2021.102719. Epub 2021 Jan 16.
The ongoing COVID-19 epidemic has spread worldwide since December 2019. Effective use of engineering controls can prevent its spread and thereby reduce its impact. As airborne transmission is an important mode of infectious respiratory disease transmission, mathematical models of airborne infection are needed to develop effective engineering control. We developed a new approach to obtain the spatial distribution for the probability of infection (PI) by combining the spatial flow impact factor (SFIF) method with the Wells-Riley model. Our method can be combined with the anti-problem approach, in order to determine the optimized arrangement of people and/or air purifiers in a confined space beyond the ability of previous methods. This method was validated by a CFD-integrated method, and an illustrative example is presented. We think our method can be helpful in controlling infection risk and making the best use of the space and equipment in built environments, which is important for preventing the spread of COVID-19 and other infectious respiratory diseases, and promoting the development of sustainable cities and society.
自2019年12月以来，持续的新冠疫情已在全球蔓延。有效利用工程控制措施可防止其传播，从而减少其影响。由于空气传播是传染性呼吸道疾病传播的一种重要方式，因此需要空气传播感染的数学模型来制定有效的工程控制措施。我们开发了一种新方法，通过将空间流动影响因子（SFIF）方法与韦尔斯-莱利模型相结合，来获得感染概率（PI）的空间分布。我们的方法可以与反问题方法相结合，以确定在先前方法能力之外的密闭空间中人员和/或空气净化器的优化布置。该方法通过CFD集成方法进行了验证，并给出了一个示例。我们认为我们的方法有助于控制感染风险，并充分利用建筑环境中的空间和设备，这对于预防新冠疫情和其他传染性呼吸道疾病的传播，以及促进可持续城市和社会的发展非常重要。
