Volume 20 Issue 3
. 
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2020  >  20(3): 139-149
LEI Bin, LIU Xing-liang, CAO Zhen, HAO Ya-rui, ZHANG Yuan, CHEN Xin-miao. Modeling and forecasting of COVID-19 spread in urban rail transit system[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 139-149. doi: 10.19818/j.cnki.1671-1637.2020.03.013
Citation: LEI Bin, LIU Xing-liang, CAO Zhen, HAO Ya-rui, ZHANG Yuan, CHEN Xin-miao. Modeling and forecasting of COVID-19 spread in urban rail transit system[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 139-149. doi: 10.19818/j.cnki.1671-1637.2020.03.013
shu

Modeling and forecasting of COVID-19 spread in urban rail transit system

doi: 10.19818/j.cnki.1671-1637.2020.03.013
  • 1.

    School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China

  • 2.

    School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Key Research and Development Program of China 2016YFC0802208

Shaanxi Transportation Technology Project 16-40K

Shaanxi Natural Science Basic Research Program 2017JQ5122

More Information
    Abstract
  • Passenger group in urban rail transit system under the COVID-19 epidemic was divided into susceptible, infected and exposed ones considering urban rail transit travel characters. In the research environment of COVID-19 free spread and the case sampling time of early spread, the infection probability of 0.41 was selected based on related studies. The urban rail transit ridership in COVID-19 case was divided into inbound/outbound phase and riding phase. Considering COVID-19 effective spread range, passengers distribution and moving characters, the COVID-19 spreading model in the urban rail transit system was built. Taking the metro system in a certain city as the simulation case, 13 patients in metro ridership cases were assumed. With the accessibility of historical passenger data, the parameters in the model were determined. The possible infections in different loading levels were forecasted, and the elements related to possible infections were discussed. Analysis result indicates that when the loading level decreases to 10% of the average level, the possible infections in most cases are less than 1, which proves the effectiveness of current urban rail transit passenger control strength. The change in possible infections caused by the reduction in passenger number in the start/terminal(less than 20%) is less than that caused by the reduction in passenger number in the vehicle(60%-80%). Therefore, the passenger density in the vehicle has more significant impact on the possible infection compared to the passenger number in the start/terminal. When passing the stop, if the ratio of the on/off board passenger numbers is no more than 1, the possible infections can be controlled effectively. If making the possible infections nonlinearly and positively correlate to the stop number, the function among the loading level, stop number and possible infections will have better performance(determination coefficient is 0.700 1).

     

    • FullText(HTML)
  • loading
    • References(30)
  • [1]
    ZHANG Jin-nong, ZHOU Lu-qian, YANG Yu-qiong, et al. Therapeutic and triage strategies for 2019 novel coronavirus disease in fever clinics[J]. The Lancet Respiratory Medicine, 2020, 8(3): 11-12. doi: 10.1016/S2213-2600(20)30071-0
    [2]
    XU Zhe, SHI Lei, WANG Yi-jin, et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome[J]. The Lancet Respiratory Medicine, 2020, 8(4): 420-422. doi: 10.1016/S2213-2600(20)30076-X
    [3]
    中国疾病预防控制中心. 新型冠状病毒肺炎流行病学特征分析[J]. 中华流行病学杂志, 2020, 41(2): 145-151. https://www.cnki.com.cn/Article/CJFDTOTAL-FBYF202003001.htm

    Chinese Center for Disease Control and Prevention. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China[J]. Chinese Journal of Epidemiology, 2020, 41(2): 145-151. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-FBYF202003001.htm
    [4]
    ROY M A, HANS H, DON K, et al. How will country-based mitigation measures influence the course of the COVID-19 epidemic?[J]. The Lancet, 10.1016/S0140-6736(20)30567-5.
    [5]
    GU Xiao-ying, CAO Bin, WANG Jian-wei. Full spectrum of COVID-19 severity still being depicted—authors' reply[J]. The Lancet 10.1016/S0140-6736(20)30371-8.
    [6]
    KANG Yun, CASTILLO-CHAVEZ C. Dynamics of SI models with both horizontal and vertical transmissions as well as Allee effects[J]. Mathematical Biosciences, 2014, 248: 97-116. doi: 10.1016/j.mbs.2013.12.006
    [7]
    SHU Hong-ying, FAN De-jun, WEI Jun-jie. Global stability of multi-group SEIR epidemic models with distributed delays and nonlinear transmission[J]. Nonlinear Analysis: Real World Applications, 2012, 13(4): 1581-1592. doi: 10.1016/j.nonrwa.2011.11.016
    [8]
    李璐, 宣慧玉. 多主体仿真在公共卫生事件应急管理中的应用——以一个传染病政策仿真系统为例[J]. 西安交通大学学报(社会科学版), 2010, 30(1): 57-64. https://www.cnki.com.cn/Article/CJFDTOTAL-XAJD201001009.htm

    LI Lu, XUAN Hui-yu. Application of multi-agent simulation to emergency management of public health events—with an epidemic control policy simulation system as an example[J]. Journal of Xi'an Jiaotong University (Social Sciences), 2010, 30(1): 57-64. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XAJD201001009.htm
    [9]
    PASTOR-SATORRAS R, VESPIGNANI A. Epidemic dynamics and endemic states in complex networks[J]. Physical Review E DOI: 10.1103/PhysRevE.63.066117.
    [10]
    NEWMAN M E J. Spread of epidemic disease on networks[J]. Physical Review E, DOI: 10.1103/PhysRevE.66.016128.
    [11]
    FUMANELLI L, AJELLI M, MERLER S, et al. Model-based comprehensive analysis of school closure policies for mitigating influenza epidemics and pandemics[J]. PLOS Computational Biology, DOI: 10.1371/journal.pcbi.1004681.
    [12]
    范如国, 王奕博, 罗明, 等. 基于SEIR的新型肺炎传播模型及拐点预测分析[J]. 电子科技大学学报, http://kns.cnki.net/kcms/detail/51.1207.T.20200221.1041.002.html.

    FAN Ru-guo, WANG Yi-bo, LUO Ming, et al. SEIR-based novel pneumonia transmission model and inflection point prediction analysis[J]. Journal of University of Electronic Science and Technology of China, http://kns.cnki.net/kcms/detail/51.1207.T.20200221.1041.002.html. (inChinese).
    [13]
    曹盛力, 冯沛华, 时朋朋. 修正SEIR传染病动力学模型应用于湖北省2019冠状病毒病(COVID-19)疫情预测和评估. 浙江大学学报(医学版)DOI: 10.3785/j.issn.1008-9292.2020.02.05.

    CAO Sheng-li, FENG Pei-hua, SHI Peng-peng. Study on the epidemic development of COVID-19 in Hubei Province by a modified SEIR model. Journal of Zhejiang University (Medical Sciences), DOI: 10.3785/j.issn.1008-9292.2020.02.05.(inChinese)
    [14]
    喻孜, 张贵清, 刘庆珍, 等. 基于时变参数-SIR模型的2019-nCoV疫情评估和预测[J]. 电子科技大学学报, DOI: 10.12178/1001-0548.2020027.

    YU Zi, ZHANG Gui-qing, LIU Qing-zhen, et al. The outbreak assessment and prediction of 2019-nCoV based on time-varying SIR Model[J]. Journal of University of Electronic Science and Technology of China, DOI: 10.12178/1001-0548.2020027.(inChinese).
    [15]
    WEI Yu, CHEN Mu-chen. Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks[J]. Transportation Research Part C: Emerging Technologies, 2012, 21(1): 148-162. doi: 10.1016/j.trc.2011.06.009
    [16]
    SUN Li-jun, LU Yang, JIN Jian-gang, et al. An integrated Bayesian approach for passenger flow assignment in metro networks[J]. Transportation Research Part C: Emerging Technologies, 2015, 52: 116-131. doi: 10.1016/j.trc.2015.01.001
    [17]
    LI Shu-kai, DESSOUKY M M, YANG Li-xing, et al. Joint optimal train regulation and passenger flow control strategy for high-frequency metro lines[J]. Transportation Research Part B: Methodological, 2017, 99: 113-137. doi: 10.1016/j.trb.2017.01.010
    [18]
    SUN Yan-shuo, SHI Jun-gang, SCHONFELD P M. Identifying passenger flow characteristics and evaluating travel time reliability by visualizing AFC data: a case study of Shanghai Metro[J]. Public Transport, 2016, 8: 341-363. doi: 10.1007/s12469-016-0137-8
    [19]
    HELBING D, JOHANSSON A, AL-ABIDEEN HZ. Dynamics of crowd disasters: an empirical study[J]. Physical Review E, DOI: 10.1103/physreve.75.046109
    [20]
    TROKO J, MYLES P, GIBSON J, et al. Is public transport a risk factor for acute respiratory infection?[J]. BMC Infectious Diseases, DOI: 10.1186/1471-2334-11-16
    [21]
    GOSCÉ L, BARTON DA W, JOHANSSON A. Analytical modelling of the spread of disease in confined and crowded spaces[J]. Scientific Reports, DOI: 10.1038/srep04856.
    [22]
    GOSCÉ L, JOHANSSON A. Analysing the link between public transport use and airborne transmission: mobility and contagion in the London underground[J]. Environmental Health, DOI: 10.1186/s12940-018-0427-5.
    [23]
    严阅, 陈瑜, 刘可伋, 等. 基于一类时滞动力学系统对新型冠状病毒肺炎疫情的建模和预测[J]. 中国科学: 数学, 2020, 50(3): 385-392. https://www.cnki.com.cn/Article/CJFDTOTAL-JAXK202003003.htm

    YAN Yue, CHEN Yu, LIU Ke-ji, et al. Modeling and prediction for the trend of outbreak of NCP based on a time-delay dynamic system[J]. Scientia Sinica: Mathematica, 2020, 50(3): 385-392. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JAXK202003003.htm
    [24]
    金启轩. 中国新冠肺炎疫情预测建模与理性评估[J]. 统计与决策, 2020(5): 11-14. https://www.cnki.com.cn/Article/CJFDTOTAL-TJJC202005003.htm

    JIN Qi-xuan. Novel coronavirus pneumonia epidemic prediction modeling and rational evaluation in China[J]. Statistics and Decision, 2020(5): 11-14. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TJJC202005003.htm
    [25]
    THOMPSON R N. Novel coronavirus outbreak in Wuhan, China, 2020: intense surveillance is vital for preventing sustained transmission in new locations[J]. Journal of Clinical Medicine, DOI: 10.3390/jcm9020498.
    [26]
    孙晨, 江亚南, 赵继敏. 新型冠状病毒肺炎流行病学特点和治疗药物研究进展[J]. 中国现代医学杂志, 2020, 30(6): 72-79. https://www.cnki.com.cn/Article/CJFDTOTAL-ZXDY202006015.htm

    SUN Chen, JIANG Ya-nan, ZHAO Ji-min. Overview on epidemiological characteristics and research of therapeutic drugs of COVID-19[J]. China Journal of Modern Medicine, 2020, 30(6): 72-79. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZXDY202006015.htm
    [27]
    TANG Biao, WANG Xia, LI Qian, et al. Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions[J]. Journal of Clinical Medicine, DOI: 10.3390/jcm9020462.
    [28]
    CHEN Wang, PETER W H, FREDERICK G H, et al. A novel coronavirus outbreak of global health concern[J]. The Lancet, 10.1016/S0140-6736(20)30185-9.
    [29]
    XU Zhou, LI Shu, TIAN Shen, et al. Full spectrum of COVID-19 severity still being depicted[J]. The Lancet, 10.1016/S0140-6736(20)30308-1.
    [30]
    HUANG Chao-lin, WANG Ye-ming, LI Xing-wang, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China[J]. The Lancet, 10.1016/S0140-6736(20)30183-5.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1362) PDF downloads(655) Cited by()
    Proportional views
    Related

    Copyright《Journal of Traffic and Transportation Engineering》编辑部陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang 'an University, Middle Section of South Second Ring Road, Xi 'an, Shaanxi（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return