ArticleInternational journal of environmental research and public health2023
Modelling Impact of High-Rise, High-Density Built Environment on COVID-19 Risks: Empirical Results from a Case Study of Two Chinese Cities.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 15 citations in OpenAlex.
- Street view-derived city built environment and vulnerability to temperature extremes: a nationally representative population-based cohort study.BMC medicine · 2025Article
- Spatiotemporal dynamics and associated drivers of COVID-19 incidence in Nepal.Tropical medicine and health · 2025Article
- Pathogenic built environment? Reflections on modeling spatial determinants of health in urban settings considering the example of COVID-19 studies.Frontiers in public health · 2025Article
- Shaping school for childhood myopia: the association between floor area ratio of school environment and myopia in China.The British journal of ophthalmology · 2024Article
- The impact of urban spatial environment on COVID-19: a case study in Beijing.Frontiers in public health · 2023Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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