Volume 20 Issue 3
. 
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2020  >  20(3): 150-158
Next Previous
ZHANG Yu, TIAN Wan-li, WU Zhong-guang, CHEN Zong-wei, WANG Ji. Transmission mechanism of COVID-19 epidemic along traffic routes based on improved SEIR model[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 150-158. doi: 10.19818/j.cnki.1671-1637.2020.03.014
Citation: ZHANG Yu, TIAN Wan-li, WU Zhong-guang, CHEN Zong-wei, WANG Ji. Transmission mechanism of COVID-19 epidemic along traffic routes based on improved SEIR model[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 150-158. doi: 10.19818/j.cnki.1671-1637.2020.03.014
shu

Transmission mechanism of COVID-19 epidemic along traffic routes based on improved SEIR model

doi: 10.19818/j.cnki.1671-1637.2020.03.014

  • Research Center for Standards and Metrology, China Academy of Transportation Sciences, Beijing 100029, China

Funds:

National Key Research and Development Program of China 2017YFF0207500

Transport Standard(Quota) Project 2019-99-069

Basic Scientific Research Project of Central Public Welfare Research Institute 20190402

Science and Technology Project of Anhui Transportation Holding Group Co., Ltd 2018basz0185

Science and Technology Planning Project of Zhejiang Province Transportation Quality Supervision Industry zj201901

More Information
  • Author Bio:

    ZHANG Yu(1979-), female, associate research fellow, zhangyu@motcats.ac.cn

  • Corresponding author: CHEN Zong-wei(1972-), male, senior engineer, chenzw@motcats.ac.cn
  • Received Date: 2020-03-05
  • Publish Date: 2020-06-25
    Abstract
  • The characteristics of COVID-19, which is transmitted by contact and droplets and is infectious in the incubation period, were considered. Combined with the narrow space and airtight environment of the vehicle and based on SEIR model, the vehicle internal epidemic transmission model was established considering the factors of virus density, contact and infection rate among passengers, and travel time. Based on the internal epidemic transmission form in the vehicle, the epidemic transmission to the non-epidemic area in the process of the vehicle loading and unloading passengers at multiple stops was considered, and a model of epidemic spread along traffic routes based on population migration was established. The transmission mechanism of the epidemic along traffic routes was analyzed using the two models. Based on the population migration index and confirmed cases in Wuhan, the relationship between confirmed cases and population migration index was analyzed, and the transmission process of the epidemic along the high-speed railway was simulated. Research result shows that the cumulative confirmed cases of each provincial and municipal level have a strong positive correlation with the population migration index, indicating that transportation has a certain role in promoting the spread of the epidemic. There may be some passengers infected within the vehicle when there are infectives. With the backward effect of incubation period, to some extent, it explains that except for Wuhan, the number of newly confirmed cases in urban areas of other provinces in China was at a peak on January 31 to February 5 in 2020. The measures such as isolation and reducing passenger occupancy to reduce the contact between passengers can effectively reduce the infection risk of passengers, and the effect is significantly better than the ventilation and disinfection measures. Therefore, in order to reasonably control the spread of the epidemic along traffic routes, some measures should be taken to reduce the occupancy rate, increase the distance between passengers and reduce the contact rate, supplemented by the measures to increase ventilation and disinfection.

     

    • FullText(HTML)
  • loading
    • References(30)
  • [1]
    Epidemiology Working Group for NCIP Epidemic Response, 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). doi: 10.3760/cma.j.issn.0254-6450.2020.02.003
    [2]
    YANG Zi-feng, ZENG Zhi-qi, WANG Ke, et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions[J]. Journal of Thoracic Disease, DOI: 10.21037/jtd.2020.02.64.
    [3]
    FENG Jia-yuan. Study on the trend prediction of the main factors affecting the occurrence and prevalence of infectious diseases[D]. Beijing: Peking Union Medical College, 2009. (in Chinese).
    [4]
    CHEOWELL G, HENGARTNER N W, CASTILLO-CHAVEZ C, et al. The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda[J]. Journal of Theoretical Biology, 2004, 229(1): 119-126. doi: 10.1016/j.jtbi.2004.03.006
    [5]
    WANG La-di. Research on the epidemic models and controlling strategy of epidemic diseases[D]. Shanghai: Shanghai University, 2004. (in Chinese).
    [6]
    LEKONE P E, FINKENSTADT B F. Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study[J]. Biometrics, 2006, 62(4): 1170-1177. doi: 10.1111/j.1541-0420.2006.00609.x
    [7]
    LIU Yun-zhong, XUAN Hui-yu, LIN Guo-xi. Mathematical and predictive models of SARS epidemic disease and mechanism of prevention and control[J]. Engineering Science, 2004, 6(9): 60-65. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GCKX200409011.htm
    [8]
    ZHANG Juan, LOU Jie, MA Zhi-en, et al. A compartmental model for the analysis of SARS transmission patterns and outbreak control measures in China[J]. Applied Mathematics and Computation, 2005, 162(2): 909-924. doi: 10.1016/j.amc.2003.12.131
    [9]
    GUO Shu-min. System research on transmission mechanism and control of some infectious diseases[D]. Beijing: The Second Academy of China Aerospace, 2010. (in Chinese).
    [10]
    CHOWELL G, CASTILLO-CHAVEZ C, FENIMORE P W, et al. Model parameters and outbreak control for SARS[J]. Emerging Infectious Diseases, 2004, 10(7): 1258-1263. doi: 10.3201/eid1007.030647
    [11]
    LIPSITCH M, COHEN T, COOPER B, et al. Transmission dynamics and control of severe acute respiratory syndrome[J]. Science, 2003, 300: 1966-1970. doi: 10.1126/science.1086616
    [12]
    BREBAN R, RIOU J, FONTANET A. Interhuman trans-missibility of middle east respiratory syndrome coronavirus: estimation of pandemic risk[J]. Lancet, 2013, 382: 694-699. doi: 10.1016/S0140-6736(13)61492-0
    [13]
    CAI Lei. Statistical modeling and analysis of important parameters based on 2014-2015 Ebola epidemic data[D]. Guangzhou: Jinan University, 2016. (in Chinese).
    [14]
    XU Zhan-kai. The building of individual-based infectious disease model and applications[D]. Beijing: Academy of Military Medical Sciences, 2016. (in Chinese).
    [15]
    ZHANG Dian-ye, GUO Han-ying. Effect of traffic and transportation preventing and controlling sever acute respiratory syndrome epidemic situation[J]. Journal of Transportation Engineering and Information, 2003, 1(1): 31-36. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JTGC200301004.htm
    [16]
    GUO Han-ying, ZHANG Dian-ye, SHI Hong-guo. Spreading theory and model study of broke-out diseases in traffic and transportation[J]. Railway Transport and Economy, 2004, 26(2): 65-67. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TDYS200402027.htm
    [17]
    CHENG Zi-long. Research on key technology of artificial transportation system modeling toward infection transmission[D]. Changsha: National University of Defense Technology, 2012. (in Chinese).
    [18]
    YANG Hua, LI Xiao-wen, SHI Hong, et al. "Fly dots" spreading model of SARS along transportation[J]. Journal of remote sensing, 2003, 7(4): 251-255. (in Chinese).
    [19]
    CAO Chun-xiang, LI Xiao-wen, YAN Jun, et al. Geo-spatial information and analysis of SARS spread trend[J]. Journal of Remote Sensing, 2003, 7(4): 241-244. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB200304000.htm
    [20]
    LIU Ya-lan, YAN Shou-yong, LI Xiao-wen, et al. Study on population migration characteristics in mainland China and its applications to decision-making for SARS control[J]. Journal of Remote Sensing, 2003, 7(4): 273-276. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB200304006.htm
    [21]
    ZHAO Shi, ZHUANG Zi-an, RAN Jin-jun, et al. The association between domestic train transportation and novel coronavirus (2019-nCoV) outbreak in China from 2019 to 2020: a data-driven correlational report[J]. Travel Medicine and Infectious Disease, 2020, 33: 101568-1-3.
    [22]
    ZHANG Yun. Modeling and analysis of influenza a (H1N1) epidemiology in mainland of China[D]. Beijing: Beijing Normal University, 2011. (in Chinese).
    [23]
    WANG L, WU J T. Characterizing the dynamics underlying global spread of epidemics[J]. Nature Communications, 2018, DOI: 10.1038/s41467-017-02344-z.
    [24]
    BROCKMANN D, HELBING D. The hidden geometry of complex network-driven contagion phenomena[J]. Science, 2013, 342: 1337-1342.
    [25]
    LIU Tao, HU Jian-xiong, KANG Min, et al. Transmission dynamics of 2019 novel coronavirus (2019-nCoV)[J]. BioRxiv, DOI: 10.1101/2020.01.25.919787.
    [26]
    WU J T, LEUNG K, LEUNG G M. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study[J]. Lancet, DOI: 10.1016/S0140-6736(20)30260-9.
    [27]
    ZHAO Shi, LIN Qian-yin, RAN Jin-jun, et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: a data-driven analysis in the early phase of the outbreak[J]. International Journal of Infectious Diseases, 2020, DOI: 10.1016/j.ijid.2020.01.050.
    [28]
    Special Expert Group for Control of the Epidemic of Novel Coronavirus Pneumonia of the Chinese Preventive Medicine Association. An update on the epidemiological characteristics of novel coronavirus pneumonia (COVID-19)[J]. Chinese Journal of Epidemiology, 2020, 41(2): 139-144. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZRYX202002002.htm
    [29]
    LIN Kun, FONG D Y T, ZHU Bi-liu, et al. Environmental factors on the SARS epidemic: air temperature, passage of time and multiplicative effect of hospital infection[J]. Epidemiology and Infection, 2006, 134(2): 223-230.
    [30]
    CHEN Wen-jiang, WU Kai-chen, WU Kai-lu, et al. Approach to the mechanism of cluster transmission of SARS by mathematical model[J]. China Tropical Medicine, 2004, 4(1): 20-23. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-RDYX200401007.htm
    • Relative Articles
    • Supplements(0)
    • Cited By

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1341) PDF downloads(563) Cited by()
    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