基于SIR模型的机动车内传染病传播研究

doi:10.16265/j.cnki.issn 1003-3033.2021.01.025

中国安全科学学报 ›› 2021, Vol. 31 ›› Issue (1): 173-178.doi: 10.16265/j.cnki.issn 1003-3033.2021.01.025

基于SIR模型的机动车内传染病传播研究

郭云龙高级工程师

辽宁警察学院治安管理系,辽宁大连 116036

收稿日期:2020-10-16 修回日期:2020-12-08 出版日期:2021-01-28 发布日期:2021-07-28
作者简介:郭云龙 (1988—),男,辽宁大连人,博士,高级工程师,主要从事交通安全、交通管理工程等方面研究。E-mail:lnpcgyl@163.com。
基金资助:
辽宁省教育厅科研经费项目(JYT2020WQ01);博士科研启动资金资助项目(2020LNPCBS-202);大连市社科联社科课题项目(2020dlskyb110);辽宁省高等学校创新团队支持计划项目(辽教函[2018]479号)。

Research on transmission of infectious diseases in motor vehicles based on SIR model

GUO Yunlong

Police Order Administration Department, Liaoning Police College, Dalian Liaoning 116036, China

Received:2020-10-16 Revised:2020-12-08 Online:2021-01-28 Published:2021-07-28

摘要/Abstract

摘要： 为控制传染病在机动车内的传播,利用传染病传播模型,研究车内传染病的传播机制和影响因素。基于传染病动力学理论,结合机动车(以公交车和小型私家车为例)的行驶特点和传播环境,针对新冠病毒(COVID-19)的传播方式,在引入病毒传播密度概念的基础上,建立公交车和小型私家车内的传染病SIR传播模型,完成不同行驶条件下的车内传播模型数值模拟仿真分析。研究结果表明:在疫情防控期间,缩短行车时间、减少车内人员交换频率、加强车内的通风和消毒、根据防控大环境适当选择公交车和私家车出行均可以对车内病毒的传播起到抑制作用。

关键词: 传染病传播, SIR模型, 机动车, 公交车, 小型私家车, 新冠病毒(COVID-19)

Abstract: In order to control spread of infectious diseases in motor vehicles, their transmission mechanism and influencing factors were studied by using infectious disease transmission model. Based on theory of infectious disease dynamics and by introducing concept of virus transmission density, SIR model of infectious diseases in buses and small private cars was established considering transmission mode of COVID-19 and driving characteristics and transmission environment of motor vehicles. Then, the model under different driving conditions were studied through numerical simulation analysis and comparison. The results show that virus transmission in cars can be suppressed during outbreak of epidemic by adopting strategies, like reducing driving time and people exchange frequency, enforcing ventilation and disinfection, and making appropriate choices for bus and private car travel according to preventative and control environment.

Key words: transmission of infectious diseases, susceptible infected recovered (SIR) model, motor vehicles, buses, small private cars, corona virus disease 2019 (COVID-19)

中图分类号:

X928.03
U492

郭云龙. 基于SIR模型的机动车内传染病传播研究[J]. 中国安全科学学报, 2021, 31(1): 173-178.

GUO Yunlong. Research on transmission of infectious diseases in motor vehicles based on SIR model[J]. China Safety Science Journal, 2021, 31(1): 173-178.

参考文献

[1] World Health Organization. WHO director-general's opening remarks at the media briefing on COVID-19-18 March 2020 [EB/OL]. [2020-03-18]. https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19-18-march-2020.
[2] 张毅,王雪成,毕清华. 基于新型冠状病毒传播机理的交通出行易感度研究[J]. 交通运输研究, 2020, 6(1): 73-80.
ZHANG Yi, WANG Xuecheng, BI Qinghua. Travel-infected susceptibility based on transmission mechanism of COVID-19[J]. Transport Research, 2020, 6(1): 73-80.
[3] 熊志华, 姚智胜. 轨道交通拥挤传播速率量化模型研究[J]. 交通运输系统工程与信息, 2018, 18(3): 146-151.
XIONG Zhihua, YAO Zhisheng. Congestion propagation quantization model about rail transit system[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(3): 146-151.
[4] 代晓旭, 胡明华, 田文, 等. 利用传染病模型研究空中交通拥挤传播规律[J]. 交通运输系统工程与信息, 2015, 15(6): 121-126.
DAI Xiaoxu, HU Minghua, TIAN Wen, et al. Mechanisms of congestion propagation in air traffic management based on infectious diseases model[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(6): 121-126.
[5] 左忠义,王英英,包蕴. 基于SIR的路边违停行为传播模型研究[J]. 大连交通大学学报, 2019, 40(3): 7-11.
ZUO Zhongyi, WANG Yingying, BAO Yun. Research on road-side violation behavior propagation model based on SIR[J]. Journal of Dalian Jiaotong University, 2019, 40(3): 7-11.
[6] 施红生. 国内重大传染病疫情铁路交通防控模式效率评估[J]. 铁路节能环保与安全卫生, 2013,3(1): 32-38.
SHI Hongsheng. Efficiency evaluation of railway traffic prevention and control mode for major infectious diseases in china[J]. Railway Energy Saving & Environmental Protection & Occupational Safety and Health, 2013,3(1): 32-38.
[7] KERMACK W O, MCKENDRICK A G. Contributions to the mathematical theory of epidemics[J]. Proceedings of the Royal Society, 1933, 141(1): 94-122.
[8] 方星, 黄培清, 黄新华. 突发事件中不实信息传播的传染病模型研究[J]. 中国安全科学学报, 2015,25(11): 150-155.
FANG Xing, HUANG Peiqing, HUANG Xinhua. Research on false information transmission in emergency based on epidemic model[J]. China Safety Science Journal, 2015,25(11): 150-155.
[9] 陈卫明, 周豪洁, 张奕莹. 基于改进传染病模型的群体情绪感染机制研究[J]. 中国安全科学学报, 2018, 28(10): 149-155.
CHEN Weiming, ZHOU Haojie, ZHANG Yiying. Study on mechanism of emotional contagion in group based on improved epidemic model[J]. China Safety Science Journal, 2018, 28(10): 149-155.
[10] 周涛, 刘权辉, 杨紫陌, 等. 新型冠状病毒感染肺炎基本再生数的初步预测[J]. 中国循证医学杂志, 2020, 20(3): 1-6.
ZHOU Tao, LIU Quanhui, YANG Zimo, et al. Preliminary prediction of the basic reproduction number of the novel coronavirus 2019-nCoV[J]. Chinese Journal of Evidence-Based Medicine, 2020, 20(3): 1-6.
[11] GB7258—2017, 机动车运行安全技术条件[S].
GB7258-2017, Technical specifications for safely of power-driven vehicles operating on roads[S].

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基于SIR模型的机动车内传染病传播研究

Research on transmission of infectious diseases in motor vehicles based on SIR model

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