Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China
Risk assessment of the intra-city spatio-temporal spreading of COVID-19 is important for providing location-based precise intervention measures, especially when the epidemic occurred in the densely populated and high mobile public places. The individual-based simulation has been proven to be an effective method for the risk assessment. However, the acquisition of individual-level mobility data is limited. This study used publicly available datasets to approximate dynamic intra-city travel flows by a spatio-temporal gravity model. On this basis, an individual-based epidemic model integrating agent-based model with the susceptible-exposed-infectious-removed (SEIR) model was proposed and the intra-city spatio-temporal spreading process of COVID-19 in eleven public places in Guangzhou China were explored. The results indicated that the accuracy of dynamic intra-city travel flows estimated by available big data and gravity model is acceptable. The spatio-temporal simulation method well presented the process of COVID-19 epidemic. Four kinds of spatial-temporal transmission patterns were identified and the pattern was highly dependent on the urban spatial structure and location. It indicated that location-based precise intervention measures should be implemented according to different regions. The approach of this research can be used by policy-makers to make rapid and accurate risk assessments and to implement intervention measures ahead of epidemic outbreaks.
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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/01436228
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Supplemental Notes:
- © 2022 Shuli Zhou et al.
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Authors:
- Zhou, Shuli
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0000-0001-8532-5581
- Zhou, Suhong
- Zheng, Zhong
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0000-0002-4899-8792
- Lu, Junwen
- Song, Tie
- Publication Date: 2022-6
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; Maps; References; Tables;
- Pagination: 102702
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Serial:
- Applied Geography
- Volume: 143
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0143-6228
- Serial URL: http://www.sciencedirect.com/science/journal/01436228
Subject/Index Terms
- TRT Terms: Communicable diseases; COVID-19; Data mining; Gravity models; Public health; Risk assessment
- Geographic Terms: Guangzhou (China)
- Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;
Filing Info
- Accession Number: 01852199
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 21 2022 11:32AM