Insights into the risk of COVID-19 infection in urban neighborhood and its environmental influence factors:A novel approach

• Published in: Sustainable cities and society 2024-07, Vol.106, p.105383, Article 105383
• Main Authors: Xiao, Peng, Zhao, Dongrui, Shen, Shouyun, Liao, Qiulin, Wang, Weiwei, Cao, Yuchi, Liao, Jingpeng, Lv, Xinyi, Liu, Yifan, Ma, Lehan, Huang, Ruiheng, Zhang, Xinxin, Shao, Xuanying, Zeng, Shuqin, Jiang, Qingchu, Chen, Jiaao
• Format: Article
• Language: English
• Subjects: Built environment; COVID-19; Healthy environment; Multi-agent modeling; Neighborhood
• ISSN: 2210-6707
• DOI: 10.1016/j.scs.2024.105383

•This new approach analyzes COVID-19 infection from a multidimensional perspective.•Constructing a multi-agent model to integrate Environment, Behavior, and infection.•Identifies infection risks, behavior patterns, and their spatio-temporal traits.•Assesses impact of control measures and environmental factors on spread.•New insights into management and disease transmission in urban environments. Many studies have dealt with the relationship between the built environment and COVID-19 infection in isolation. However, the research approaches were often inadequate in accurately reflecting the risk of COVID-19 infection. Therefore, we aim to develop a novel approach to study the risk of COVID-19 infection and its Environmental Influence Factors (EIFs) at the neighborhood scale. We constructed a multi-agent model of the spatial environment of residential quarters, including models of behavior and COVID-19 infection dynamics, and established 24 experimental schemes based on the Prevention and Control Measures (PCMs) of zonal control, mask-wearing, and outing frequency to simulate and assess the infection risk. The results show that the areas of accessible wood, hard surfaces, and seating are high-risk sites for infection. Zonal control and mask-wearing are effective PCMs, and the EIFs we set all play significant roles in influencing infection risk. Our study provides insight into the effects of PCMs and EIFs related to COVID-19 infection and highlights that the novel approach not only reveals the levels of infection risk and its mechanisms but also detects the spatio-temporal characteristics of behaviors and infection. This novel approach, dealing with individuals, space, environment, behavior, PCMs, and COVID-19 infection dynamics in integration, overcomes the limitations of traditional methods that overlook the influences of the spatio-temporal dynamics of individual behaviors and their environmental factors. It offers theoretical support for designing healthy outdoor built environments and technical support for the PCMs of the epidemic.