|Table of Contents|

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

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2020年03期
Page:
150-158
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Transmission mechanism of COVID-19 epidemic along traffic routes based on improved SEIR model
Author(s):
ZHANG Yu;  TIAN Wan-li;  WU Zhong-guang;  CHEN Zong-wei;  WANG Ji
(Research Center for Standards and Metrology, China Academy of Transportation Sciences, Beijing 100029, China)
Keywords:
traffic management;  COVID-19;  epidemic transmission mechanism;  improved SEIR model;  traffic route
PACS:
U491.112
DOI:
10.19818/j.cnki.1671-1637.2020.03.014
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. 1 tab, 5 figs, 30 refs.

References:

[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.(in Chinese)
[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] 冯佳园.传染病发生和流行的主要影响因素趋势预测研究[D].北京:北京协和医学院,2009.
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.
[5] 王拉娣.传染病动力学模型就控制策略研究[D].上海:上海大学,2004.
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.
[7] 刘云忠,宣慧玉,林国玺.SARS传染病数学建模及预测预防控制机理研究[J].中国工程科学,2004,6(9):60-65.
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)
[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.
[9] 郭树敏.传染性疾病传染机制与控制的系统研究[D].北京:中国航天第二研究院,2010.
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.
[11] LIPSITCH M, COHEN T, COOPER B, et al. Transmission dynamics and control of severe acute respiratory syndrome[J]. Science, 2003, 300: 1966-1970.
[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.
[13] 蔡 磊.基于2014～2015年埃博拉疫情数据的统计建模与重要参数分析[D].广州:暨南大学,2016.
CAI Lei. Statistical modeling and analysis of important parameters based on 2014-2015 Ebola epidemic data[D]. Guangzhou: Jinan University, 2016.(in Chinese)
[14] 徐展凯.基于个体的传染病传染模型构建及应用[D].北京:人民解放军军事医学科学研究院,2016.
XU Zhan-kai. The building of individual-based infectious disease model and applications[D]. Beijing: Academy of Military Medical Sciences, 2016.(in Chinese)
[15] 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 preventing and controlling sever acute respiratory syndrome epidemic situation[J]. Journal of Transportation Engineering and Information, 2003, 1(1): 31-36.(in Chinese)
[16] 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.(in Chinese)
[17] 程子龙.面向传染病传染的人工交通系统建模关键技术研究[D].长沙:国防科学技术大学,2012.
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] 杨 华,李小文,施 宏,等.SARS沿交通线路的“飞点”传染模型[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.(in Chinese)
[19] 曹春香,李小文,闫 珺,等.地理空间信息与SARS疫情走势[J].遥感学报,2003,7(4):241-244.
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)
[20] 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 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)
[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] 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,2011.
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] 中华预防医学会新型冠状病毒肺炎防控专家组.新型冠状病毒肺炎流行病学特征的最新认识[J].中华流行病学杂志,2020,41(2):139-144.
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)
[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] 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.(in Chinese)

Memo

Memo:
-
Last Update: 2020-07-10