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COVID-19 risk assessment driven by urban spatiotemporal big data: a case study of Guangdong-Hong Kong-Macao Greater Bay Area
- Source :
- Acta Geodaetica et Cartographica Sinica, Vol 49, Iss 6, Pp 671-680 (2020)
- Publication Year :
- 2020
- Publisher :
- Surveying and Mapping Press, 2020.
-
Abstract
- The rapid spread of the novel coronavirus (COVID-19) from late 2019 to early 2020 poses a huge challenge to the public health of China and the world. The risk assessment of COVID-19 plays an essential role in the decision making of epidemic prevention. As one of the most important metropolitan areas in China, Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is seriously affected by COVID-19. A massive number of returnees after the holidays further poses potential COVID-19 risks. Targeting on the urgent need of COVID-19 risk assessment in GBA, we combine multi-source urban spatiotemporal big data and traditional epidemiological model to design an improved model. Specifically, the improved model introduces dynamic “return-to-work” population and propagation hotspots to calibrate COVID-19 parameters in different assessment units and improve SEIR model suitability in GBA; targeting on the urgent needs of high resolution (e.g. community level) risk assessment, the model utilizes multi-source urban big data (e.g, mobile phone) to improve modelling spatial resolution from more detailed population and COVID-19 OD matrix. The simulation results show that: ① compared with the traditional SEIR model, the proposed model has better capability for risk assessment in GBA; ② the massive population flow in GBA introduces considerable COVID-19 risk in GBA; ③ a variety of epidemic prevention initiatives in China are highly effective for delaying the spread of COVID-19 in GBA.
Details
- Language :
- Chinese
- ISSN :
- 10011595
- Volume :
- 49
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- Acta Geodaetica et Cartographica Sinica
- Accession number :
- edsair.doajarticles..bd2e57028e6d0a1aa3912b133553e171