Abstract
Characteristics of the urban environment (e.g., building density and road network) can influence the spread and transmission of coronavirus disease 2019 (COVID-19) within cities, especially in high-density high-rise built environments. Therefore, it is necessary to identify the key attributes of high-density high-rise built environments to enhance modelling of the spread of COVID-19. To this end, case studies for testing attributes for modelling development were performed in two densely populated Chinese cities with high-rise, high-density built environments (Hong Kong and Shanghai).The investigated urban environmental features included 2D and 3D urban morphological indices (e.g., sky view factor, floor area ratio, frontal area density, height to width ratio, and building coverage ratio), socioeconomic and demographic attributes (e.g., population), and public service points-of-interest (e.g., bus stations and clinics). The modelling effects of 3D urban morphological features on the infection rate are notable in urban communities. As the spatial scale becomes larger, the modelling effect of 2D built environment factors (e.g., building coverage ratio) on the infection rate becomes more notable. The influence of several key factors (e.g., the building coverage ratio and population density) at different scales can be considered when modelling the infection risk in urban communities. The findings of this study clarify how attributes of built environments can be applied to predict the spread of infectious diseases. This knowledge can be used to develop effective planning strategies to prevent and control epidemics and ensure healthy cities. © 2023 by the authors. Licensee MDPI, Basel, Switzerland.
| Original language | English |
|---|---|
| Article number | 1422 |
| Journal | International Journal of Environmental Research and Public Health |
| Volume | 20 |
| Issue number | 2 |
| Online published | 12 Jan 2023 |
| DOIs | |
| Publication status | Published - Jan 2023 |
| Externally published | Yes |
Funding
This research was supported by grants from National Natural Science Foundation of China (No. 42271477, 42101241 and 42071394) and the grant from Natural Science Foundation of Guangdong Province, China (No. 2021A1515012571).
Research Keywords
- COVID-19
- infection rate
- population
- urban environment
Publisher's Copyright Statement
- This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/