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
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
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:
ZHANG Hao1,2, YIN Ling1,*(), LIU Kang1,5, MAO Liang3, FENG Shengzhong4, CHEN Jie5(
), MEI Shujiang6
Received:
2021-02-23
Revised:
2021-03-22
Online:
2021-11-25
Published:
2022-01-25
Contact:
*YIN Ling, E-mail: yinling@siat.ac.cnSupported by:
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。
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, 2021, 23(11): 1936-1945.DOI:10.12082/dqxxkx.2021.210090
ZHANG Hao, YIN Ling, LIU Kang, MAO Liang, FENG Shengzhong, CHEN Jie, MEI Shujiang. Effectiveness of Non-pharmaceutical Interventions on Suppressing the 1st Wave of COVID-19 Epidemic in Shenzhen: An Agent-based Modelling Study[J]. Journal of Geo-information Science, 2021, 23(11): 1936-1945.DOI:10.12082/dqxxkx.2021.210090
表2
深圳市第一波疫情期间实施的各项非药物干预措施
序号 | 措施 | 描述 | 模型实现 | |
---|---|---|---|---|
1 | 集中诊治 | 显性感染者确诊后进行医院集中隔离,直至至恢复;隐性感染者如果核酸检测阳性,则同样集中隔离至核酸检测呈阴性 | 个体从模拟系统中移除 | |
2 | 综合隔 离措施 | 密接追踪 | 确诊患者的密切接触者集中隔离14 d* | 对确诊患者的密接者集中隔离14 d,不与任何人产生接触;若密接者未被感染则在14 d后释放,否则从模拟系统移除 |
输入人员居家隔离 | 进入城市的人员居家隔离14 d | 仅与家庭成员产生接触 | ||
发病后居家隔离 | 具有疑似症状的患者发病后自行居家隔离 | 发病后仅与家庭成员产生接触(病毒传播率下降 | ||
3 | 居家令 | 居民限制外出活动 | 个体仅与家庭成员产生接触 | |
4 | 佩戴口罩 | 外出佩戴口罩 | 降低易感者被感染的风险(病毒传播率下降θ,即模型中的口罩有效性) | |
5 | 分批复工 | 按时间段和工作性质分批复工 | 有工作的个体从2月10日至3月2日平均分为4批复工,复工前执行居家令 | |
6 | 城际交通限制 | 武汉限制外出 | 限制武汉地区人员进入深圳 | 影响模型的潜在输入性病例 |
湖北省其他城市限制外出 | 限制湖北省人员进入深圳 | |||
限制其余地区入深人员 | 限制其余地区进入深圳的人员数量 | 仅将模型选中的已复工个体放入模拟系统 |
[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.
pmid: 32340094 |
[ 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. ]
doi: 10.3760/cma.j.cn112338-20200316-00360 pmid: 32340094 |
|
[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.
doi: 10.1186/s40249-020-00714-2 |
[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.
doi: 10.1016/S2589-7500(20)30165-5 |
[5] |
Lai S, Ruktanonchai N W, Zhou L, et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China[J]. Nature, 2020, 585(7825):410-413.
doi: 10.1038/s41586-020-2293-x |
[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.
doi: 10.1126/science.abb3221 |
[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.
doi: 10.1073/pnas.2004978117 |
[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.
doi: 10.1016/j.chaos.2020.110088 |
[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.
doi: 10.1038/s41467-020-19393-6 |
[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.
doi: 10.1016/j.imu.2020.100403 pmid: 32835081 |
[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.
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.
doi: 10.1038/nature04017 |
[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.
doi: 10.1016/j.compenvurbsys.2010.03.004 |
[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.
doi: 10.12082/dqxxkx.2021.200449 |
[ 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.
doi: 10.1016/S1473-3099(20)30162-6 |
[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.
doi: 10.1038/s41562-020-0931-9 |
[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.
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: 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.
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.
doi: 10.1038/s41598-019-51609-8 |
[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: A systematic review and meta-analysis[J]. Clinical Infectious Diseases, 2017, 65(11):1934-1942.
doi: 10.1093/cid/cix681 |
[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.
doi: S1201-9712(20)30139-9 pmid: 32179137 |
[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.
doi: 10.1007/s11427-020-1661-4 |
[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.
doi: 10.1056/NEJMoa2001316 |
[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.
doi: 10.1016/S1473-3099(20)30144-4 |
[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.
doi: 10.1016/S2468-2667(20)30133-X |
[34] |
IHME COVID-19 Forecasting Team. Modeling COVID-19 scenarios for the united states[J]. Nature Medicine, 2021, 27(1):94-105.
doi: 10.1038/s41591-020-1132-9 |
[35] |
Worby C J, Chang H H. Face mask use in the general population and optimal resource allocation during the COVID-19 pandemic[J]. Nature Communications, 2020, 11(1):4049.
doi: 10.1038/s41467-020-17922-x |
[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.
doi: 10.1016/S0140-6736(20)31142-9 |
[37] |
Chan J F W, 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.
doi: 10.1093/cid/ciaa644 |
[38] |
Prather K A, Wang C C, Schooley R T. Reducing transmission of SARS-CoV-2[J]. Science, 2020, 368(6498):1422-1424.
doi: 10.1126/science.abc6197 |
[1] | 刘慧敏, 刘青豪, 陈袁芳, 石岩, 邓敏. 顾及时空邻近的恐怖团伙关系发现方法[J]. 地球信息科学学报, 2021, 23(4): 584-592. |
[2] | 谢聪慧, 吴世新, 张晨, 孙文涛, 何海芳, 裴韬, 罗格平. 基于谱系聚类的全球各国新冠疫情时间序列特征分析[J]. 地球信息科学学报, 2021, 23(2): 236-245. |
[3] | 巫细波, 赖长强, 葛志专. 政府严控期我国地级市COVID-19疫情的时空集聚、演变及自相关效应研究[J]. 地球信息科学学报, 2021, 23(2): 246-258. |
[4] | 毕佳, 王贤敏, 胡跃译, 罗孟涵, 张俊华, 胡凤昌, 丁子洋. 一种基于改进SEIR模型的突发公共卫生事件风险动态评估与预测方法——以欧洲十国COVID-19为例[J]. 地球信息科学学报, 2021, 23(2): 259-273. |
[5] | 韦原原, 江南, 陈云海, 李响, 杨振凯. 顾及时空对象空间相互作用的疫情风险评估建模与应用[J]. 地球信息科学学报, 2021, 23(2): 274-283. |
[6] | 方云皓, 顾康康. 基于多元数据的中国地理空间疫情风险评估探索——以2020年1月1日至4月11日COVID-19疫情数据为例[J]. 地球信息科学学报, 2021, 23(2): 284-296. |
[7] | 曹中浩, 张健钦, 杨木, 贾礼朋, 邓少存. 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. |
[8] | 杜毅贤, 徐家鹏, 钟琳颖, 侯盈旭, 沈婕. 网络舆情态势及情感多维特征分析与可视化——以COVID-19疫情为例[J]. 地球信息科学学报, 2021, 23(2): 318-330. |
[9] | 张琛, 马祥元, 周扬, 郭仁忠. 基于用户情感变化的新冠疫情舆情演变分析[J]. 地球信息科学学报, 2021, 23(2): 341-350. |
[10] | 崔明洁, 姚霞, 方昊然, 张杨成思, 杨德刚, 裴韬. SARS与COVID-19时空传播差异性及影响因素分析[J]. 地球信息科学学报, 2021, 23(11): 1910-1923. |
[11] | 李照, 高惠瑛, 代晓奕, 孙海. 一种耦合LSTM算法和云模型的疫情传播风险预测模型[J]. 地球信息科学学报, 2021, 23(11): 1924-1925. |
[12] | 杨飞, 华一新, 李响, 李坡, 杨振凯, 曹一冰. 基于多粒度时空对象数据模型的城市基础设施建模与管理[J]. 地球信息科学学报, 2021, 23(11): 1984-1997. |
[13] | 尹凌, 刘康, 张浩, 奚桂锴, 李璇, 李子垠, 薛建章. 耦合人群移动的COVID-19传染病模型研究进展[J]. 地球信息科学学报, 2021, 23(11): 1894-1909. |
[14] | 朱净萱, 戴强, 蔡俊逸, 朱少楠, 张书亮. 基于多智能体的城市洪涝灾害动态脆弱性计算模型构建[J]. 地球信息科学学报, 2021, 23(10): 1787-1797. |
[15] | 葛咏, 刘梦晓, 胡姗, 任周鹏. 时空统计学在贫困研究中的应用及展望[J]. 地球信息科学学报, 2021, 23(1): 58-74. |
|