Xia, J;
Zhou, Y;
Li, Z;
Li, F;
Yue, Y;
Cheng, T;
Li, Q;
(2020)
COVID-19 risk assessment driven by urban spatiotemporal big data: a case study of Guangdong-Hong Kong-Macao Greater Bay Area.
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica
, 49
(6)
pp. 671-680.
10.11947/j.AGCS.2020.20200080.
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城市时空大数据驱动的新型冠状病毒传播风险评估 以粤港澳大湾区为例.pdf - Published Version Download (4MB) |
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.
| Type: | Article |
|---|---|
| Title: | COVID-19 risk assessment driven by urban spatiotemporal big data: a case study of Guangdong-Hong Kong-Macao Greater Bay Area |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.11947/j.AGCS.2020.20200080 |
| Publisher version: | http://xb.sinomaps.com/EN/10.11947/j.AGCS.2020.202... |
| Language: | Chinese |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10105114 |
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