ArticleScientific reports2021
Diverse and nonlinear influences of built environment factors on COVID-19 spread across townships in China at its initial stage.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review.The Science of the total environment · 2022Pooled it
- Features of the land use and COVID-19 cases and deaths in urban and land counties in Poland throughout the pandemic: a machine learning approach.BMC public health · 2026Article
- Spatiotemporal dynamics and associated drivers of COVID-19 incidence in Nepal.Tropical medicine and health · 2025Article
- Potential physical distance in the open urban grey space of city counties in Poland and COVID-19 cases and deaths throughout the pandemic.Archives of public health = Archives belges de sante publique · 2025Article
- A coupled model for public health risk: hazard and urban vulnerability in 18 cities in Sichuan, China.Frontiers in public health · 2025Article
- Article
- Spatiotemporal hierarchical Bayesian analysis to identify factors associated with COVID-19 in suburban areas in Colombia.Heliyon · 2024Article
- Article
- Spatial and deep learning analyses of urban recovery from the impacts of COVID-19.Scientific reports · 2023Article
- Residential Environment Assessment by Older Adults in Nursing Homes during COVID-19 Outbreak.International journal of environmental research and public health · 2022Article
- Do the determinants of COVID-19 transmission differ by epidemic wave? Evidence from U.S. counties.Cities (London, England) · 2022Article
- Lessons learnt for enhancing hospital resilience to pandemics: A qualitative analysis from Italy.International journal of disaster risk reduction : IJDRR · 2022Article
- Spatial-Temporal Pattern Evolution of Public Sentiment Responses to the COVID-19 Pandemic in Small Cities of China: A Case Study Based on Social Media Data Analysis.International journal of environmental research and public health · 2022Article
- Spatio-temporal distribution pattern of COVID-19 in the Northern Italy during the first-wave scenario: The role of the highway network.Transportation research interdisciplinary perspectives · 2022Article
- Neighborhood-level COVID-19 hospitalizations and mortality relationships with built environment, active and sedentary travel.Health & place · 2021Article
- "What should be computed" for supporting post-pandemic recovery policymaking? A life-oriented perspective.Computational urban science · 2021Review
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3 authors.
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Abstract
The built environment can contribute to the spread of the novel coronavirus disease (COVID-19) by facilitating human mobility and social contacts between infected and uninfected individuals. However, mobility data capturing detailed interpersonal transmission at a large scale are not available. In this study, we aimed to objectively assess the influence of key built environment factors, which create spaces for activities-"inferred activity" rather than "actually observed activity"-on the spread of COVID-19 across townships in China at its initial stage through a random forest approach. Taking data for 2994 township-level administrative units, the spread is measured by two indicators: the ratio of cumulative infection cases (RCIC), and the coefficient of variation of infection cases (CVIC) that reflects the policy effect in the initial stage of the spread. Accordingly, we selected 19 explanatory variables covering built environment factors (urban facilities, land use, and transportation infrastructure), the level of nighttime activities, and the inter-city population flow (from Hubei Province). We investigated the spatial agglomerations based on an analysis of bivariate local indicators of spatial association between RCIC and CVIC. We found spatial agglomeration (or positive spatial autocorrelations) of RCIC and CVIC in about 20% of all townships under study. The density of convenience shops, supermarkets and shopping malls (DoCSS), and the inter-city population flow (from Hubei Province) are the two most important variables to explain RCIC, while the population flow is the most important factor in measuring policy effects (CVIC). When the DoCSS gets to 21/km
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