Built environment and early infection of COVID-19 in urban districts: A case study of Huangzhou

Highlights

• Relationships between built environment attributes and early infection of COVID-19 are proposed as the key concerns for prevention and control.

• Confirmed COVID-19 infectious cases in the initial outbreak are identified geographically in Huangzhou district through GIS mapping.

• Clusters of confirmed COVID-19 infectious cases are identified based on DBSCAN.

• Direct and indirect effects of transportation infrastructure, commercial prosperity, and medical service are quantitatively estimated.

• The socioeconomic characteristics indirectly influence the potential risk of COVID-19 contagion.

Abstract

Since COVID-19 spread rapidly worldwide, many countries have experienced significant growth in the number of confirmed cases and deaths. Earlier studies have examined various factors that may contribute to the contagion rate of COVID-19, such as air pollution, smoking, humidity, and temperature. As there is a lack of studies at the neighborhood-level detailing the spatial settings of built environment attributes, this study explored the variations in the size of the COVID-19 confirmed case clusters across the urban district Huangzhou in the city of Huanggang. Clusters of infectious cases in the initial outbreak of COVID-19 were identified geographically through GIS methods. The hypothetic relationships between built environment attributes and clusters of COVID-19 cases have been investigated with the structural equation model. The results show the statistically significant direct and indirect influences of commercial vitality and transportation infrastructure on the number of confirmed cases in an infectious cluster. The clues ch inducing a high risk of contagions have been evidenced and provided for the decision-making practice responding to the initial stage of possible severe epidemics, indicating that the local public health authorities should implement sufficient measures and adopt effective interventions in the areas and places with a high probability of crowded residents.