ArticleThe Science of the total environment2020
Early forecasting of the potential risk zones of COVID-19 in China's megacities.
Article in The Science of the total environment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 113 citations in OpenAlex.
- Applications of GIS and geospatial analyses in COVID-19 research: A systematic review.F1000Research · 2020Pooled it
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- One Health Ethics and the Ethics of Zoonoses: A Silent Call for Global Action.Veterinary sciences · 2024Review
- Assessing eco-geographic influences on COVID-19 transmission: a global analysis.Scientific reports · 2024Article
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- Spatiotemporal pattern and suitable areas analysis of equine influenza in global scale (2005-2022).Frontiers in veterinary science · 2024Article
- Application of wastewater-based surveillance and copula time-series model for COVID-19 forecasts.The Science of the total environment · 2023Article
- An algorithm to estimate the real time secondary infections in sub-urban bus travel: COVID-19 epidemic experience at Chennai Metropolitan city India.Virusdisease · 2023Article
- A novel textual track-data-based approach for estimating individual infection risk of COVID-19.Risk analysis : an official publication of the Society for Risk Analysis · 2023Article
- Review
- Unequal impact of the COVID-19 pandemic on mental health: Role of the neighborhood environment.Sustainable cities and society · 2022Article
- Social Factors as Major Determinants of Rural Development Variation for Predicting Epidemic Vulnerability: A Lesson for the Future.International journal of environmental research and public health · 2022Article
- Use and design of public green spaces in Serbian cities during the COVID-19 pandemic.Habitat international · 2022Article
- Analyzing COVID-19's impact on the travel mobility of various social groups in China's Greater Bay Area via mobile phone big data.Transportation research. Part A, Policy and practice · 2022Article
- Agricultural Openness and the Risk of COVID-19 Incidence: Evidence from China.International journal of environmental research and public health · 2022Article
- Spatial and temporal variability and data bias in wastewater surveillance of SARS-CoV-2 in a sewer system.The Science of the total environment · 2022Article
- Article
- Comparative Study of Government Response Measures and Epidemic Trends for COVID-19 Global Pandemic.Risk analysis : an official publication of the Society for Risk Analysis · 2022Article
- An evaluative model for assessing pandemic resilience at the neighborhood level: The case of Tehran.Sustainable cities and society · 2021Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, the coronavirus disease 2019 (COVID-19) has become a worldwide public health threat. Early and quick identification of the potential risk zones of COVID-19 infection is increasingly vital for the megacities implementing targeted infection prevention and control measures. In this study, the communities with confirmed cases during January 21-February 27 were collected and considered as the specific epidemic data for Beijing, Guangzhou, and Shenzhen. We evaluated the spatiotemporal variations of the epidemics before utilizing the ecological niche models (ENM) to assemble the epidemic data and nine socioeconomic variables for identifying the potential risk zones of this infection in these megacities. Three megacities were differentiated by the spatial patterns and quantities of infected communities, average cases per community, the percentages of imported cases, as well as the potential risks, although their COVID-19 infection situations have been preliminarily contained to date. With higher risks that were predominated by various influencing factors in each megacity, the potential risk zones coverd about 75% to 100% of currently infected communities. Our results demonstrate that the ENM method was capable of being employed as an early forecasting tool for identifying the potential COVID-19 infection risk zones on a fine scale. We suggest that local hygienic authorities should keep their eyes on the epidemic in each megacity for sufficiently implementing and adjusting their interventions in the zones with more residents or probably crowded places. This study would provide useful clues for relevant hygienic departments making quick responses to increasingly severe epidemics in similar megacities in the world.
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