Abstract
2019年末至2020年初新型冠状病毒(COVID-19)的快速传播对中国与世界的公共卫生带来巨大的挑战。如何科学合理地评估新型冠状病毒传播风险并制定相应防疫管控措施,是各国所面临的难题,也是科学防治与精准施策的重要依据之一。作为我国最重要的城市群之一,粤港澳大湾区受本次新型冠状病毒影响较大,且春节假期后大量的复工回流人口进一步带来潜在的传播风险。本文面向粤港澳大湾区新型冠状病毒传播风险评估的紧迫需求,结合大湾区多源城市时空大数据与流行病动力学模型,构建适宜大湾区的改进模型,并对新型冠状病毒在大湾区的传播风险和各类防疫管控措施效果进行评估与模拟。首先,引入动态复工回流人口和聚集热点改进现有动力学模型(SEIR模型),对现有动力学模型在不同空间评估单元的传播参数进行纠偏,加强模型在大湾区评估中的适宜性;利用手机信令等多源城市大数据,构建更精细化的人口、疾病流动矩阵和相应的传染病动力学模型,以满足各级防疫部门精细化(如村(社区)级)风险评估的迫切需求。模拟结果表明,相对经典SEIR模型,改进模型在大湾区的传播风险评估中具有更强的适宜性;大湾区高强度的人口流动为病毒的传播带来较高的风险;防疫部门所采取各类管控措施对病毒在大湾区的传播具有较强的抑制作用。Alternate 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.
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