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摘要: 为研究交通运输对新型冠状病毒肺炎(COVID-19)传播的影响, 建立了基于系统动力学的交通运输传播新型冠状病毒肺炎模型; 在设定模型假设的基础上, 将人群分为易感个体、潜伏个体、感染个体和移除个体4种, 并根据COVID-19的传播特性建立了交通运输影响下疫区、港站、车船和关联地区的COVID-19修正易感-潜伏-感染-移除(SEIR)和易感-潜伏-感染(SEI)传播动力学方程; 在研究交通运输传播COVID-19过程的基础上, 绘制了交通运输传播COVID-19的系统动力学模型因果回路, 并进行了因果反馈回路分析; 构建了基于系统动力学的交通运输传播COVID-19模型, 并检验了模型的量纲一致性、机械错误、有效性和极端条件; 以A市为研究对象, 建立了3种仿真场景以研究交通运输对COVID-19传播的影响。仿真结果表明: A市COVID-19感染个体数量在疫情发生后第32天达到峰值, 累计感染77 726人, A市以外地区感染个体数量在疫情发生后第30天达到峰值, 累计感染28 084人; 交通运输对COVID-19的传播起正反馈作用, 交通运输影响下的COVID-19感染个体和移除个体数量峰值分别是无交通运输参与下的18.62、10.99倍, 因此, 实施交通管控措施对控制疫情发展、防范疫情扩散具有积极作用; 公共交通出行比例的增加将进一步加快COVID-19的传播, 因此, 减少人们出行需求, 降低公共交通出行比例, 加大对公共交通车船、港站的病毒消杀工作能有效减缓COVID-19的传播。Abstract: To research the influence of traffic and transportation on the spread of corona virus disease 2019(COVID-19), the model of COVID-19 spreaded by traffic and transportation was established based on the system dynamics. Based on the hypothesis of model, the population was divided into the susceptible individual, explored individual, infected individual and removed individual. According to the spreading characteristics of COVID-19, the modified susceptible-explored-infected-removed(SEIR) and susceptible-explored-infected(SEI) spreading dynamics equations of COVID-19 of epidemic area, port, station, car, ship and related area were established under the influence of traffic and transportation. Based on the analysis of spread process of COVID-19 by traffic and transportation, a causal loop of the system dynamics model of traffic and transportation spreading the COVID-19 was drawn, and a causality feedback loop was analyzed. The model of COVID-19 spreaded by traffic and transportation was established based on the system dynamics, and the dimensional consistency, mechanical error, model validity, and extreme condition of the model were tested. Taking the City A as the research example, the influence of traffic and transportation on the spread of COVID-19 was researched by establishing three simulation scenarios. Simulation result shows that the number of individuals infected with the COVID-19 in City A reaches a peak on the 32 nd day after the outbreak, and 77 726 people were infected, and that of the area outside the City A reaches the peak on the 30 th day after the outbreak, and 28 084 people were infected. The traffic and transportation have positive feedback effects on the spread of COVID-19. The peak volumes of infected and removed individuals of COVID-19 under the influence of traffic and transportation are 18.62 and 10.99 times of those without the participation of traffic and transportation, respectively. Therefore, the traffic control measures have a positive effect on controlling the development and preventing the spread of COVID-19. The increase of public transport proportion will accelerate the spread of COVID-19. Therefore, reducing people's travel demands and the proportion of public transport, and increasing the virus elimination work of public transportation car, ship, port and station can effectively slow down the spread of COVID-19.
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Key words:
- traffic system /
- spread of COVID-19 /
- system dynamics /
- modeling and simulation /
- causal loop /
- stock and flow
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图 2 COVID-19交通车船、港站传播转化
Figure 2. Transformation of COVID-19 spreading in transportation car, ship, port and station
图 4 交通运输传播COVID-19系统动力学模型因果回路
Figure 4. Causal loop of system dynamics model of spread of COVID-19 by traffic and transportation
图 5 交通运输传播COVID-19系统动力学模型存量流量
Figure 5. Stock and flow of system dynamics model of spread of COVID-19 by traffic and transportation
图 10 交通运输对COVID-19传播的影响
Figure 10. Influence of traffic and transportation on spread of COVID-19
图 11 A市公共交通出行比例对COVID-19传播的影响
Figure 11. Influence of public transport proportion of City A on spread of COVID-19
图 12 关联地区公共交通出行比例对COVID-19传播的影响
Figure 12. Influence of public transport proportion of related area on spread of COVID-19
表 1 主要参数取值
Table 1. Main parameters values
参数名称 初始取值 取值来源与说明 公共交通出行比例/% 50.93 《湖北省城市道路交通文明畅通提升行动计划(2018~2020年)》 关联地区总人口/万人 139 096.65 国家统计局统计年鉴 疫区总人口/万人 908.35 国家统计局统计年鉴 关联地区感染个体接触率 6 参考SARS感染个体接触率及关联地区人口密度综合取值 港站感染个体接触率 20.64 由港站人口密度与A市人口密度比值确定 疫区感染个体接触率 17.20 参考SARS感染个体接触率及A市人口密度综合取值 车船感染个体接触率 24.08 由车船人口密度与A市人口密度比值确定 疫区感染个体数量/人 27.00 A市卫生健康管理部门网站2019年12月31日数据 感染率 0.173 2 新增确诊病例与累计追踪到密切接触者的比值 关联地区潜伏个体接触率 12.00 参考SARS潜伏个体接触率及关联地区人口密度综合取值 港站潜伏个体接触率 41.16 由港站人口密度与A市人口密度比值确定 疫区潜伏个体接触率 34.30 参考SARS潜伏个体接触率及A市人口密度综合取值 车船潜伏个体接触率 48.02 由车船人口密度与A市人口密度比值确定 疫区潜伏个体数量/人 323 A市卫生健康管理部门网站2019年12月31日数据 转化率 1/7 《新型冠状病毒肺炎诊疗方案(试行第六版)》 
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