Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (1): 141-149.doi: 10.13229/j.cnki.jdxbgxb20210587

Previous Articles    

Modeling urban bus transit spreading the epidemic under public health emergencies

Shao-jie WU1,2,3(),Jian SUN1,2,3()   

  1. 1.College of Transportation Engineering，Chang'an University，Xi'an 710021，China
    2.College of Future Transportation，Chang'an University，Xi'an 710021，China
    3.Smart City and Intelligent Transportation Interdisciplinary Center，Chang'an University，Xi'an 710021，China
  • Received:2021-06-26 Online:2023-01-01 Published:2023-07-23
  • Contact: Jian SUN E-mail:wushaojie@chd.edu.cn;jiansun@chd.edu.cn

RICH HTML

 1   

PDF (PC)

132

Abstract:

To reveal the influence of urban public transportation on the dispersion of COVID-19 within the city， an F-SEIR model considering migrants is established to analyze the impact of inter-regional population movement on the spread of the epidemic. The epidemic transmission characteristics within enclosed public buses were considered， on which， an COVID-19 influenced model of urban public transportation communication was established. Using IC card data and GPS data of public transportation in Wuhan， the empirical case study was simulated with each of the 160 towns in Wuhan as the analysis units. The results demonstrated that under the influence of public transportation， the spread of urban epidemic is accelerated and the phenomenon of "flying point transmission" appears in many towns， and the number of infected people attains 70 446. Timely countermeasures to suspend public transportation have successfully reduced the number of infected persons by 18.64% to 28.34%， and the earlier the measures are taken， the larger total number of infected persons are reduced. The model accurately fits the evolutionary trends of COVID-19. By comparing the simulation results from different dates after the shutdown of publication transportation service on Jan. 23， 2020， the goodness-of-fit between the fitted data and the official epidemic data attains as high as 0.9675. The model is helpful to understand the impact of public transportation on the spread of the epidemic within cities， which may further provide theoretical and decision-making guidance for risk evaluation and policy stipulating during the different stages of other major public health events.

Key words: major public health events, COVID-19, urban transport, SEIR model, bus IC card data

CLC Number: 

  • U121

Fig.1

Schematic diagram of F-SEIR model"

Fig.2

Schematic diagram of COVID-19 spreading model with urban transportation system"

Fig.3

Density distribution of bus stops by town in Wuhan， China"

Fig.4

Passenger travel chain situations"

Fig.5

Model implementation process"

Fig.6

Trend of infected people with different infection rates"

Fig.7

Trend of infected and cured people under extreme condition"

Fig.8

Spatial distribution of infected persons by town in Wuhan， China"

Fig.9

Trend of infected persons under three different conditions"

Table 1

Part of the simulation and official released data"

日期仿真感染人数官方公布感染数相对误差/%
2月12日41 35132 99425.33
2月15日44 73739 46213.37
2月18日47 23944 4126.36
2月21日48 85345 6606.99
2月24日49 92247 0716.05
2月27日50 61948 1375.16
3月01日50 94049 3153.30
3月04日51 11249 6712.90
3月07日51 17049 9122.52
3月10日51 18649 9782.42
1 中国疾病预防控制中心新型冠状病毒肺炎应急响应机制流行病学组. 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.
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.
2 Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China[J]. The Lancet, 2020, 395(10223): 497-506.
3 Nian G Y, Peng B Z, Sun D J, et al. Impact of COVID-19 on urban mobility during post-epidemic period in megacities: from the perspectives of taxi travel and social vitality[J]. Sustainability, 2020, 12(19):No.12197954.
4 张殿业, 郭寒英. 交通运输通道防控非典型肺炎(SARS)疫情的作用研究[J]. 交通运输工程与信息学报, 2003, 1(1): 31-36.
Zhang Dian-ye, Guo Han-ying. Effect of traffic and transportation prevention and controlling sever acute respiratory syndrome epidemic situation[J]. Journal of Transportation Engineering and Information, 2003, 1(1): 31-36.
5 郭寒英, 张殿业, 石红国. 交通运输突发疫情扩散理论与模型研究[J]. 铁道运输与经济, 2004, 26(2):65-67.
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.
6 杨华, 李小文, 施宏,等. SARS沿交通线的"飞点"传播模型[J]. 遥感学报, 2003, 7(4): 251-255.
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.
7 刘亚岚, 阎守邕, 李小文,等. 中国内地人口流动空间规律研究及其在SARS控制宏观决策中的应用[J]. 遥感学报, 2003, 7(4): 273-276.
Liu Yan-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.
8 张云. 中国大陆地区甲型H1N1流感疫情传播的建模与分析[D]. 北京:北京师范大学地理科学学部, 2011.
Zhang Yun. Modeling and analysis of influenza a (H1N1) epidemiology in mainland of China[D]. Beijing: Beijing Normal University, Faculty of Geographical Science, 2011.
9 Fang L Q, Vlas S J D, Feng D, et al. Geographical spread of SARS in mainland China[J]. Tropical Medicine & International Health, 2009, 14(Sup.1): 14⁃20.
10 Khan K, Arino J, Hu W, et al. Spread of a novel influenza A (H1N1) virus via global airline transportation[J]. N Engl J Med, 2009, 361(2): 212-214.
11 Anderson R M, Heesterbeek H, Klinkenberg D D, et al. How will country-based mitigation measures influence the course of the COVID-19 epidemic[J]. The Lancet, 2020, 395(10228): 931-934.
12 Roosa K, Lee Y, Luo R, et al. Real-time forecasts of the 2019-nCoV epidemic in China from February 5th to February 24th, 2020[J]. Infectious Disease Modelling, 2020, 5:256-263.
13 Tang B, Wang X, Li Q, et al. Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions[J]. Journal of Clinical Medicine,2020,9(2): No.9020462.
14 程晓敏, 李芊璘, 陆家海. 从2019新型冠状病毒病疫情谈新发传染病防控策略[J]. 传染病信息, 2020, 33(1): 22-26.
Cheng Xiao-min, Li Qian-lin, Lu Jia-hai. Prevention and control strategies for emerging infectious diseases based on the outbreak of coronavirus disease 2019[J]. Infectious Disease Information, 2020, 33(1): 22-26.
15 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.
16 You C, Deng Y H, Hu W J, et al. Estimation of the time-varying reproduction number of COVID-19 outbreak in China[J]. International Journal of Hygiene and Environmental Health, 2020, 228: No.113555.
17 Yang Z, Zeng Z, Wang K,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,2020,12(3): 165-174.
18 中华人民共和国国家卫生健康委员会. 新型冠状病毒肺炎诊疗方案(试行第六版)[J]. 中国感染控制杂志, 2020, 19(2): 192-195.
National Health Commission of the People's Republic of China.Diagnosis and treatment plan for novel coronavirus pneumonia(trial version 6)[J]. Chinese Journal of Infection Control, 2020, 19(2): 192-195.
19 靳英辉,蔡林,程真顺,等.新型冠状病毒(2019-nCoV)感染的肺炎诊疗快速建议指南(标准版)[J]. 解放军医学杂志, 2020, 45(1): 1-20.
Jin Ying-hui, Cai Lin, Cheng Zhen-shun, et al. A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version)[J]. Medical Journal of Chinese People's Liberation Army, 2020, 45(1): 1-20.
20 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.
21 Zhang J, Lou J, Ma Z, et al. A compartmental model for the analysis of SARS transmission patterns and outbreak control measures in China[J]. Applied Mathematics & Computation, 2005, 162(2): 909-924.
22 Lin K, Fong D, Zhu B, 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.
23 陈文江,吴开琛,吴开录,等.运用数学模型探讨SARS聚集性传播的机制[J].中国热带医学, 2004, 4(1): 20-23.
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.
24 张宇, 田万利, 吴忠广,等. 基于改进SEIR模型的新冠肺炎疫情沿交通线路传播机制[J]. 交通运输工程学报, 2020, 20(3): 150-158.
Zhang Yu, Tian Wan-li, Wu Zhong-guang, et al. Study on transmission mechanism of COVID-19 epidemic along traffic lines based on improved SEIR model[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 150-158.
25 国家统计局. 第六次全国普查汇总数据[R]. 北京:中国统计出版社,2010.
26 李佳怡, 张锦, 张静文,等. 城市公交乘客下车站点推算方法和有效性评价[J]. 武汉大学学报:信息科学版, 2018, 43(8): 1172-1177.
Li Jia-yi, Zhang Jin, Zhang Jing-wen, et al. An algorithm to identify passengers' alighting stations and the effectiveness evaluation[J]. Geomatics and Information Science of Wuhan University, 2018, 43(8): 1172-1177.
[1] Bao-feng SUN,Yuan JIANG,Li-li ZHENG,Wan-kun CUI,Xin-xin REN. Modified design model of maintenance network for urban rail transit system with variable radius covering [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 526-534.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd