地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (11): 1936-1945.doi: 10.12082/dqxxkx.2021.210090

• 专栏:全球新型冠状病毒肺炎(COVID-19)疫情时空建模与决策分析 • 上一篇    下一篇

深圳市快速抑制COVID-19疫情的非药物干预措施效果评估:基于智能体的建模研究

张浩1,2, 尹凌1,*(), 刘康1,5, 毛亮3, 冯圣中4, 陈洁5(), 梅树江6   

  1. 1. 中国科学院深圳先进技术研究院,深圳 518055
    2. 中国科学院大学,北京 100049
    3. 佛罗里达大学地理系,盖恩斯维尔 32611-2002
    4. 国家超级计算深圳中心,深圳 518055
    5. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    6. 深圳市疾病预防控制中心,深圳 518055
  • 收稿日期:2021-02-23 修回日期:2021-03-22 出版日期:2021-11-25 发布日期:2022-01-25
  • 通讯作者: *尹凌(1981— ),女,重庆人,研究员,主要从事时空数据挖掘、传染病时空建模、交通GIS等研究。 E-mail: yinling@siat.ac.cn
  • 作者简介:张浩(1989— ),男,河南永城人,博士生,主要从事时空大数据挖掘与基于智能体的传染病建模研究。E-mail: hao.zhang1@siat.ac.cn
  • 基金资助:
    国家自然科学基金项目(41771441);国家自然科学基金项目(41901391);自治区重大科技专项(2020A03004-4);广东省自然科学基金面上项目(2021A1515011191);资源与环境信息系统国家重点实验室开放课题(2019);比尔及梅琳达·盖茨基金(INV-005834)

Effectiveness of Non-pharmaceutical Interventions on Suppressing the 1st Wave of COVID-19 Epidemic in Shenzhen: An Agent-based Modelling Study

ZHANG Hao1,2, YIN Ling1,*(), LIU Kang1,5, MAO Liang3, FENG Shengzhong4, CHEN Jie5(), MEI Shujiang6   

  1. 1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Department of Geography, University of Florida, Gainesville 32611-2002, USA
    4. National Supercomputing Center in Shenzhen, Shenzhen 518055, China
    5. State Key Laboratory of Resource and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    6. Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, China
  • Received:2021-02-23 Revised:2021-03-22 Online:2021-11-25 Published:2022-01-25
  • Contact: *YIN Ling, E-mail: yinling@siat.ac.cn
  • Supported by:
    National Natural Science Foundation of China, No(41771441);National Natural Science Foundation of China, No(41901391);Major science and technology projects of Xinjiang Uygur Autonomous Region, No(2020A03004-4);Natural Science Foundation of Guangdong Province, No(2021A1515011191);State Key Laboratory of Resources and Environmental Information System, No(2019);Bill & Melinda Gates Foundation, No(INV-005834)

摘要:

在2020年COVID-19第一波疫情中,通过一系列非药物干预措施,国内许多城市实现了疫情的快速抑制。对这些交叉叠加的多项干预措施进行单项措施的效果评估,识别出关键的防控策略,能够为未来的疫情防控提供重要的经验与科学依据。本研究以深圳市为例,利用融合了多源时空轨迹大数据的空间显式智能体模型评估深圳市快速抑制第一波疫情的各项非药物干预措施效果,识别出核心措施与辅助措施。模拟结果显示,在深圳市第一波疫情中,单项干预措施有效性从高到低依次为居家令、综合隔离、佩戴口罩与分批复工。其中,居家令或综合隔离均能有效抑制疫情的大范围暴发,被本研究称之为核心措施;佩戴口罩或分批复工则只能从不同程度上降低总体感染规模并延缓疫情峰值,并不能抑制疫情暴发,被本研究称之为辅助措施。考虑到社会经济成本以及常态化防疫中人群依从性降低,本研究建议在COVID-19 散发疫情防控中将核心措施与辅助措施相结合,重点实施各项隔离措施,同时将外出佩戴口罩作为疫情常态化防控手段。此外,本研究展示了结合时空大数据与智能体模型精细化模拟城市内部传染病扩散过程的优势:不仅能在城市内部高精度推演疫情发展过程,而且能够支撑评估面向个体及各类型出行活动的非药物干预措施实施效果,为制定针对性、精细化的“时间-空间-人群”防控策略提供重要的科学依据。

关键词: COVID-19, 时空数据, 智能体模型, 传染病建模, 居家令, 隔离, 口罩

Abstract:

Many cities in China have adopted a series of Non-Pharmaceutical Interventions (NPIs) and rapidly suppressed the 1st wave of COVID-19 epidemic in 2020. It is critical to evaluate the effectiveness of these NPIs for future epidemic control. However, as a variety of NPIs were applied together in practice, it is difficult to evaluate the effectiveness of a single type of intervention by epidemiological observation. Taking Shenzhen city as an example, this study used a spatially explicit agent-based model by integrating mobile phone location data, travel survey data, building survey data and other multi-source spatiotemporal big data to evaluate the effectiveness of different types of NPIs in the suppression of the 1st wave of COVID-19 epidemic in Shenzhen. The simulation results show that the peak of the epidemic would have appeared on the 127th day since Jan 1st of 2020, resulting in an average of 72.26% of the population to be infected without any interventions. In the 1st wave of Shenzhen epidemic, except for the hospitalization of confirmed cases and intercity traffic restrictions, the stay-at-home order was the most effective one, followed by comprehensive isolation and quarantine measures (for close contacts, imported population and suspected cases), mask wearing, and orderly resumption of work. The stay-at-home order and comprehensive isolation and quarantine measures can effectively control the large-scale outbreak of the COVID-19, which are identified as the core measures; Mask wearing and orderly resumption of work can only reduce the overall infection size and delay the epidemic peak, which are identified as secondary measures. Considering the socioeconomic costs and the receding compliance to interventions in the post-epidemic period, this study suggests that the core measures and secondary measures should be combined to control the sporadic cases. Specifically, the local government can give the highest priority to isolation and quarantine measures for confirmed cases and high-risk individuals, complemented by mask wearing. In addition, our model can reveal the high-risk infection areas at a community level, which can help deploy control measures within an urban environment. In summary, this study demonstrated the advantages of integrating spatiotemporal big data and agent-based models to simulate the spread processes of infectious diseases in an urban environment: it can not only simulate the evolving processes of an epidemic at a fine-grained scale, but also evaluate the effectiveness of the NPIs at an individual level and for activity-travel behaviors, which can be useful for precise intervention.

Key words: COVID-19, spatiotemporal data, agent-based model, epidemic modeling, stay-at-home order, quarantine, mask