ArticleCities (London, England)2023
Quantifying COVID-19 recovery process from a human mobility perspective: An intra-city study in Wuhan.
Article in Cities (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Mobility census for monitoring rapid urban development.Journal of the Royal Society, Interface · 2024Article
- Gauging urban resilience in the United States during the COVID-19 pandemic via social network analysis.Cities (London, England) · 2023Article
- Non-commuting intentions during COVID-19 in Nanjing, China: A hybrid latent class modeling approach.Cities (London, England) · 2023Article
- Spatial and deep learning analyses of urban recovery from the impacts of COVID-19.Scientific reports · 2023Article
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Authors and funding
6 authors.
Funding
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Abstract
The COVID-19 pandemic has brought huge challenges to sustainable urban and community development. Although some recovery signals and patterns have been uncovered, the intra-city recovery process remains underexploited. This study proposes a comprehensive approach to quantify COVID-19 recovery leveraging fine-grained human mobility records. Taking Wuhan, a typical COVID-19 affected megacity in China, as the study area, we identify accurate recovery phases and select appropriate recovery functions in a data-driven manner. We observe that recovery characteristics regarding duration, amplitude, and velocity exhibit notable differences among urban blocks. We also notice that the recovery process under a one-wave outbreak lasts at least 84 days and has an S-shaped form best fitted with four-parameter Logistic functions. More than half of the recovery variance can be well explained and estimated by common variables from auxiliary data, including population, economic level, and built environments. Our study serves as a valuable reference that supports data-driven recovery quantification for COVID-19 and other crises.
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