Assessing and controlling infection risk with Wells-Riley model and spatial flow impact factor (SFIF)

Yong Guo; Hua Qian; Zhiwei Sun; Jianping Cao; Fei Liu; Xibei Luo; Ruijie Ling; Louise B Weschler; Jinhan Mo; Yinping Zhang

doi:10.1016/j.scs.2021.102719

Assessing and controlling infection risk with Wells-Riley model and spatial flow impact factor (SFIF)

Sustain Cities Soc. 2021 Apr:67:102719. doi: 10.1016/j.scs.2021.102719. Epub 2021 Jan 16.

Authors

Yong Guo^{1

2}, Hua Qian³, Zhiwei Sun^{1

2}, Jianping Cao⁴, Fei Liu⁵, Xibei Luo⁵, Ruijie Ling⁵, Louise B Weschler⁶, Jinhan Mo^{1

2}, Yinping Zhang^{1

2}

Affiliations

¹ Department of Building Science, Tsinghua University, Beijing, China.
² Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Beijing, China.
³ School of Energy and Environment, Southeast University, Nanjing, China.
⁴ School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou, China.
⁵ Hubei Provincial Hospital of Integrated Chinese and Western Medicine, Wuhan, China.
⁶ Independent Researcher, Colts Neck, NJ, USA.

Abstract

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.

Keywords: CFD; Infection risk distribution; Spatial flow impact factor; Wells-Riley model.