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

doi:10.11947/j.AGCS.2020.20200080

测绘学报 ›› 2020, Vol. 49 ›› Issue (6): 671-680.doi: 10.11947/j.AGCS.2020.20200080

• 地图学与地理信息 • 下一篇

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

夏吉喆^1,2,3, 周颖⁴, 李珍^1,3, 李帆^3,5, 乐阳^1,2,3, 程涛⁶, 李清泉^1,2,3,7

1. 深圳大学建筑与城市规划学院城市空间信息工程系, 广东深圳 518060;
2. 人工智能与数字经济广东省实验室(深圳), 广东深圳 518060;
3. 广东省城市空间信息工程重点实验室, 广东深圳 518060;
4. 深圳大学公共卫生学院, 广东深圳 518060;
5. 深圳大学计算机与软件学院, 广东深圳 518060;
6. 英国伦敦大学土木、环境与测绘工程系, 英国伦敦 WC1E6BT;
7. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079

收稿日期:2020-03-05 修回日期:2020-04-09 出版日期:2020-06-20 发布日期:2020-06-28
通讯作者: 李清泉 E-mail:liqq@szu.edu.cn
作者简介:夏吉喆(1987-),男,博士,助理教授,研究方向为时空数据结构、计算、建模与智慧城市的应用。E-mail:xiajizhe@szu.edu.cn
基金资助:
国家重点研发计划（2018YFB2100704）；国家自然科学基金（41701444；7181101150）

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

XIA Jizhe^1,2,3, ZHOU Ying⁴, LI Zhen^1,3, LI Fan^3,5, YUE Yang^1,2,3, CHENG Tao⁶, LI Qingquan^1,2,3,7

1. Department of Urban Informatics, School of Architecture and Urban Planning,Shenzhen University, Shenzhen 518060, China;
2. Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518060, China;
3. Guangdong Key Laboratory for Urban Informatics, Shenzhen University, Shenzhen 518060, China;
4. College of public health, Shenzhen University, Shenzhen 518060, China;
5. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
6. Department of Civil, Environmental and Geomatic Engineering, University College London, London WC1E6BT, UK;
7. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China

Received:2020-03-05 Revised:2020-04-09 Online:2020-06-20 Published:2020-06-28
Supported by:
The National Key Research and Development Program of China (No. 2018YFB2100704);The National Natural Science Foundation of China (Nos. 41701444;7181101150)

摘要/Abstract

摘要： 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。

关键词: 新型冠状病毒, 粤港澳大湾区, 时空大数据, 流行病动力学模型

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.

Key words: COVID-19, Guangdong-Hong Kong-Macao Greater Bay Area, spatiotemporal big data, epidemiological model

中图分类号:

P208

夏吉喆, 周颖, 李珍, 李帆, 乐阳, 程涛, 李清泉. 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[J]. 测绘学报, 2020, 49(6): 671-680.

XIA Jizhe, ZHOU Ying, LI Zhen, LI Fan, YUE Yang, CHENG Tao, LI Qingquan. COVID-19 risk assessment driven by urban spatiotemporal big data: a case study of Guangdong-Hong Kong-Macao Greater Bay Area[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(6): 671-680.

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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

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

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