COVID-19 risk assessment driven by urban spatiotemporal big data: a case study of Guangdong-Hong Kong-Macao Greater Bay Area

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