Assessment of respiratory disease infection risk and natural ventilation intervention countermeasures in teaching spaces: A campus case study. (1st July 2023)

• Record Type: Journal Article
• Authors: Li, Sui; Qin, Feifei; Dong, Yixin; Zhou, Shiwen; Sun, Jiayi
• Is Part Of: Journal of building engineering. Volume 70(2023)
• Publication Date: 2023-07-01
• Subjects: COVID-19 -- Wells-riley model -- Basic reproduction number -- Infection risk assessment -- Built environment intervention countermeasures; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2023.106369
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: The risk of indoor respiratory disease transmission can be significantly reduced through interventions that target the built environment. Several studies have successfully developed theoretical models to calculate the effects of built environment parameters on infection rates. However, current studies have mainly focused on calculating infection rate values and comparing pre- and post-optimization values, lacking a discussion of safe baseline values for infection rates with risk class classification. The purpose of this paper is to explore the design of interventions in the built environment to improve the ability of buildings to prevent virus transmission, with a university campus as an example. The study integrates the Wells-Riley model and basic reproduction number to identify teaching spaces with high infection risk on campus and proposes targeted intervention countermeasures based on the analysis of critical parameters. The results showed that teaching buildings with a grid layout pattern had a higher potential risk of infection under natural ventilation. By a diversity of building environment interventions designed, the internal airflow field of classrooms can be effectively organized, and the indoor virus concentration can be reduced. We can find that after optimizing the building mentioned above and environment intervention countermeasures, the maximum indoor virus infection probability can be reduced by 22.88%, and the basic reproduction number can be reduced by 25.98%, finally reaching a safe level of less than 1.0. In this paper, we support university campuses' respiratory disease prevention and control programs by constructing theoretical models and developing parametric platforms. Graphical abstract: Image 1 Highlights: Combining the Well-Riley formula and Basic Reproduction Number concepts. Identification and assessment of campus teaching spaces with high infection risk. Visualization of indoor infection risk levels based on parameterization techniques. Develop a dynamic control model for indoor respiratory disease infection risk.