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

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.