Identifying COVID-19 transmission clusters and their spatially heterogeneous associations with built-environment features at the neighborhood scale

Keywords: Infectious disease, Transmission risk, Built environment, Spatial heterogeneity, Shanghai, MST-DBSCAN, GPR, GWPR
Abstract Type: Virtual Paper Abstract

Abstract

Cluster infections are crucial to COVID-19's rapid spread. Identifying the transmission clusters and their influencing factors is critical for developing targeted interventions. This study aims to identify COVID-19 clusters in the Omicron wave of Shanghai between March 6 and March 17, 2022, and examine the spatially heterogeneous effects of built-environment features on the size of COVID-19 clusters. We applied a Modified Space–Time Density-Based Spatial Clustering of Application with Noise (MST-DBSCAN) to detect COVID-19 clusters. And then the global Poisson regression (GPR) model and geographically weighted Poisson regression (GWPR) model were used to explore the influencing factors. The results revealed that specific areas in the suburbs are more vulnerable to larger clusters. The proportion of commercial, public service, industrial land, and metro station centrality are positively and significantly associated with the size of COVID-19 clusters, while land use mix, green and open spaces density, education level, and distances to hospitals are negatively associated. Further, noticeable disparities exist in the effects of built-environment attributes on COVID-19 risk between the central and suburban areas. This study provides implications for developing place-specific measures to mitigate the emergence of large-scale COVID-19 clusters.