Associating COVID-19 Severity with Urban Factors: A Case Study of Wuhan

Abstract

Wuhan encountered a serious attack in the first round of the coronavirus disease 2019 (COVID-19) pandemic, which has resulted in a public health social impact, including public mental health. Based on the Weibo help data, we inferred the spatial distribution pattern of the epidemic situation and its impacts. Seven urban factors, i.e., urban growth, general hospital, commercial facilities, subway station, land-use mixture, aging ratio, and road density, were selected for validation with the ordinary linear model, in which the former six factors presented a globally significant association with epidemic severity. Then, the geographically weighted regression model (GWR) was adopted to identify their unevenly distributed effects in the urban space. Among the six factors, the distribution and density of major hospitals exerted significant effects on epidemic situation. Commercial facilities appear to be the most prevalently distributed significant factor on epidemic situation over the city. Urban growth, in particular the newly developed residential quarters with high-rise buildings around the waterfront area of Hanyang and Wuchang, face greater risk of the distribution. The influence of subway stations concentrates at the adjacency place where the three towns meet and some near-terminal locations. The aging ratio of the community dominantly affects the hinterland of Hankou to a broader extent than other areas in the city. Upon discovering the result, a series of managerial implications that coordinate various urban factors were proposed. This research may contribute toward developing specific planning and design responses for different areas in the city based on a better understanding of the occurrence, transmission, and diffusion of the COVID-19 epidemic in the metropolitan area.

Review of Associations between Built Environment Characteristics and Severe Acute Respiratory Syndrome Coronavirus 2 Infection Risk

“…Besides, in some recreation and service facilities, such as restaurants, hotels, and bars, people tend to take off their masks to talk, drink, and dine [ 38 ]. Furthermore, a large number of the commercial locations are designed as indoor spaces with inadequate ventilation, which can easily become high-risk places because the spread of the virus is intensified in confined spaces [ 41 ]. Hence, urban areas with intensive commercial facilities may be confronted with a higher virus infection risk.…”

Revisión rápida: probabilidad de contagio por infecciones respiratorias agudas en el transporte público colectivo

“…Li y colaboradores encontraron que la severidad de la epidemia por SARS-CoV-2 fue mayor en las regiones estudiadas de Wuhan con mayor densidad de estaciones del metro (=0.086, p=0.004). 16…”

Relationship between Built Environment and COVID-19 Dispersal Based on Age Stratification: A Case Study of Wuhan

“…Most of them mainly focus on the spatial distribution of different built environment factors and the epidemic situation, or only study one of the factors in depth. Li proposed and verified that six factors, including urban growth, hospital density, commercial facilities, subway stations, mixed land use and urban population aging degree were positively correlated with the severity of COVID-19 [ 29 ]. Yip’s study in the Hong Kong area found that the number of restaurants, public markets and clinics is positively related to the number of COVID-19 reported cases [ 30 ].…”