EN中文

地球信息科学学报 >

0 20210090 - 20210090

DOI: https://doi.org/10.12082/dqxxkx.2021.210090

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 ,
  • 尹凌 ,
  • 刘康 ,
  • 毛亮 ,
  • 冯圣中 ,
  • 陈洁 ,
  • 梅树江
展开
  • 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
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

收稿日期: 2021-02-23

  修回日期: 2021-03-22

  网络出版日期: 2021-09-07

基金资助

国家自然科学基金项目（41771441、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)

收起

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

  • ZHANG Hao ,
  • YIN Ling ,
  • LIU Kang ,
  • MAO Liang ,
  • FENG Shengzhong ,
  • CHEN Jie ,
  • MEI Shujiang
Expand
  • 1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen518055, China;
    2. University of Chinese Academy of Sciences, Beijing100049, China;
    3. Department of Geography, University of Florida, Gainesville32611-2002, USA;
    4. National Supercomputing Center in Shenzhen, Shenzhen518055, China;
    5. State Key Laboratory of Resource, Environmental Information Systems, Institute of Geographic Sciences, Natural Resources Research, Chinese Academy of Sciences, Beijing100101, China;
    6. Shenzhen Center for Disease Control, Prevention, Shenzhen518055, China

Received date: 2021-02-23

  Revised date: 2021-03-22

  Online published: 2021-09-07

Supported by

National Natural Science Foundation of China, No.41771441, 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

Fold

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

本文引用格式

张浩 , 尹凌 , 刘康 , 毛亮 , 冯圣中 , 陈洁 , 梅树江 . 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[J]. 地球信息科学学报, 0 : 20210090 -20210090 . DOI: 10.12082/dqxxkx.2021.210090

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 finegrained 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

参考文献

[1] 新华网.超大型城市战“疫”的深圳样本[ED/OL]. http://www.xinhuanet.com/politics/2020-04/20/c_1125879663.htm,2020-12-01.
[ Xinhuanet. Shenzhen sample of megacity for fight Coronavirus[ED/OL]. http://www.xinhuanet.com/politics/2020-04/20/c_1125879663.htm, 2020-12-01. ]
[2] 邹旋,吴永胜,刘晓剑,等.深圳市新型冠状病毒肺炎应急响应策略和措施效果评价[J].中华流行病学杂志,2020, 41(8):1225-1230.
[ Zou X, Wu Y S, Liu X J, et al.Evaluation of the emergency response strategies and measures on the epidemic of COVID-19 in Shenzhen, China[J]. Chinese Journal of Epidemiology, 2020,41(8):1225-1230. ]
[3] Zou H C, Shu Y L, Feng T J. How Shenzhen, China avoided widespread community transmission: A potential model for successful prevention and control of COVID-19[J]. Infectious Diseases of Poverty, 2020,9(1):89.
[4] Zhou Y, Xu R, Hu D, et al.Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: A modelling study using mobile phone data[J]. The Lancet Digital Health, 2020,2(8):e417-e424.
[5] Lai S, Ruktanonchai N W, Zhou L, et al.Effect of nonpharmaceutical interventions to contain COVID- 19 in China[J]. Nature, 2020,585(7825):410-413.
[6] Li R, Pei S, Chen B, et al.Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)[J]. Science, 2020,368(6490):489-493.
[7] Gatto M, Bertuzzo E, Mari L, et al.Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures[J]. Proceedings of the National Academy of Sciences, 2020,117(19):10484-10491.
[8] Silva P C L, Batista P V C, Lima H S, et al. COVID-ABS: an agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions[ J]. Chaos, Solitons, and Fractals, 2020,139:110088.
[9] Chang S L, Harding N, Zachreson C, et al.Modelling transmission and control of the COVID-19 pandemic in Australia[J]. Nature Communications, 2020,11(1):5710.
[10] Mahdizadeh Gharakhanlou N, Hooshangi N.Spatio-temporal simulation of the novel coronavirus (COVID- 19) outbreak using the agent-based modeling approach (case study: Urmia, Iran)[J]. Informatics in Medicine Unlocked, 2020,20:100403.
[11] Giacopelli G.A full-scale agent-based model of Lombardy COVID-19 dynamics to explore social networks connectivity and vaccine impact on epidemic[J]. MedRxiv, 2020. DOI: https://doi.org/10.1101/2020.09.13.20193599.
[12] 刘涛,黎夏,刘小平.基于小世界网络的多智能体及传染病时空传播模拟[J].科学通报,2009,54(24):3834-3843.
[ Liu T, Li X, Liu X P.Integration of small world networks with multi- agent systems for simulating epidemic spatiotemporal transmission[J]. Chinese Science Bulletin, 2009,54(24):3834-3843. ]
[13] 潘理虎,秦世鹏,李晓文,等. COVID-19 病毒防控多智能体仿真模型[J].系统仿真学报,2020,32(11):2244-2257.
[ Pan L H, Qin S P, Li X W, et al.Multi-agent simulation model for COVID- 19 virus prevention and control[J]. Journal of System Simulation, 2020,32(11):2244-2257. ]
[14] Ferguson N M, Cummings D A, Cauchemez S, et al.Strategies for containing an emerging influenza pandemic in Southeast Asia[J]. Nature, 2005,437(7056):209-214.
[15] Mao L, Bian L.Spatial-temporal transmission of influenza and its health risks in an urbanized area[J]. Computers, Environment and Urban Systems, 2010,34(3):204-215.
[16] 曹中浩,张健钦,杨木,等.基于GIS新冠智能体仿真模型及应用——以广州市为例[J].地球信息科学学报,2021, 23(2):297-306.
[ Cao Z H, Zhang J Q, Yang M, et al.The City agent model of COVID-19 based on GIS and application: A case study of Guangzhou[J]. Journal of Geo-information Science, 2021,23(2):297-306. ]
[17] Koo J R, Cook A R, Park M, et al.Interventions to mitigate early spread of SARS-CoV-2 in Singapore: A modelling study[J]. The Lancet Infectious Diseases, 2020,20(6): 678-688.
[18] Ferguson N, Laydon D, Nedjati Gilani G, et al.Report 9: Impact of Non-Pharmaceutical Interventions (NPIs) to reduce COVID19 mortality and healthcare demand[J]. Imperial College London, 2020,10(77482):491-497.
[19] Aleta A, Martín-Corral D, Pastore Y Piontti A, et al. Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19[J]. Nature Human Behaviour, 2020,4(9):964-971.
[20] Müller S A, Balmer M, Charlton W, et al.A realistic agent-based simulation model for COVID-19 based on a traffic simulation and mobile phone data[J]. arXiv, 2020. DOI: arXiv:2011.11453.
[21] Yin L, Zhang H, Li Y, et al.Effectiveness of contact tracing, mask wearing and prompt testing on suppressing COVID- 19 resurgences in megacities: An individual- Based modelling study[J]. SSRN, 2021. DOI: Effectiveness of contact tracing, mask wearing and prompt testing on suppressing COVID- 19 resurgences in megacities: An individual- Based modelling study[J]. SSRN, 2021. DOI: http://dx.doi.org/10.2139/ssrn.3765491.
[22] 深圳市卫生健康委员会.疫情信息[EB/OL].http://wjw.sz.gov.cn/yqxx/,2020- 03-31.
[ Shenzhen Municipal Health Commission, Epidemic situation in Shenzhen[EB/OL]. http://wjw.sz.gov.cn/yqxx/, 2020-03-31. ]
[23] Xie J, Yin L, Mao L.A Modeling framework for individual- based urban mobility based on data fusion[A]. 2018 26th International Conference on Geoinformatics, IEEE, 2018:1-6.
[24] Yin L, Lin N, Zhao Z.Mining daily activity chains from large- scale mobile phone location data[J]. Cities, 2020. DOI: https://doi.org/103010.101016/j.cities.102020.103013.
[25] Zhang J, Klepac P, Read J M, et al.Patterns of human social contact and contact with animals in Shanghai, China[J]. Scientific Reports, 2019,9(1):15141.
[26] 百度. 百度迁徙数据[EB/OL].http://qianxi.baidu.com/, 2020-03-01.
[ Baidu, Baidu mobility data[EB/OL]. http://qianxi.baidu.com/, 2020-03-01. ]
[27] Offeddu V, Yung C F, Low M S F, et al. Effectiveness of masks and respirators against respiratory infections in healthcare workers:Asystematic review and meta-analysis[J]. Clinical Infectious Diseases, 2017,65(11):1934-1942.
[28] Nishiura H, Kobayashi T, Miyama T, et al.Estimation of the asymptomatic ratio of novel coronavirus infections (COVID- 19)[J]. International Journal of Infectious Diseases, 2020,94:154-155.
[29] Mizumoto K, Zarebski A, Chowell G.Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID- 19) cases on board the Diamond Princess cruise ship, Yokohama, Japan[J]. Euro Surveill, 2020,25(10):2000180.
[30] Hu Z L, Song C, Xu C J, et al.Clinical characteristics of 24 asymptomatic infections with COVID- 19 screened among close contacts in Nanjing, China[J]. Science China Life Sciences, 2020,63(5):706-711.
[31] Li Q, Guan X, Wu P, et al.Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia[ J]. The New England Journal of Medicine, 2020,382(13):1199-1207.
[32] Kucharski A J, Russell T W, Diamond C, et al.Early dynamics of transmission and control of COVID- 19: A mathematical modelling study[J]. The Lancet Infectious Diseases, 2020,20(5):553-558.
[33] Davies N G, Kucharski A J, Eggo R M, et al.Effects of non- pharmaceutical interventions on COVID- 19 cases, deaths, and demand for hospital services in the UK: a modelling study[J]. The Lancet Public Health, 2020,5(7): e375-e385.
[34] IHME COVID-19 Forecasting Team. Modeling COVID- 19 scenarios for the united states[J]. Nature Medicine, 2021,27(1):94-105.
[35] WorbyCJ, ChangHH. Face mask use in the general population and optimal resource allocation during the COVID-19 pandemic[J]. Nature Communications, 2020,11(1):4049.
[36] Chu D K, Akl E A, Duda S, et al.Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: A systematic review and meta- analysis[J]. Lancet, 2020,395(10242):1973-1987.
[37] Chan J FW, Yuan S F, Zhang A J, et al.Surgical mask partition reduces the risk of noncontact transmission in a golden Syrian hamster model for coronavirus disease 2019 (COVID-19)[J]. Clinical Infectious Diseases, 2020, 71(16):2139-2149.
[38] Prather K A, Wang C C, Schooley R T.Reducing transmission of SARS-CoV-2[J]. Science, 2020,368(6498):1422-1424.
Options
文章导航

/

〈 〉