突发公共卫生事件下城市公交传播疫情模型

doi:10.13229/j.cnki.jdxbgxb20210587

摘要/Abstract

摘要：

为了揭示城市公交运行对城市内新型冠状病毒肺炎（COVID?19）传播的影响，通过建立考虑迁入者的F-SEIR模型，针对区域人口流动对疫情传播的影响进行分析。在考虑密闭公共交通工具内部疫情传播特性的基础上，建立了城市公交传播COVID?19模型。利用武汉市公交IC卡数据和公交车辆GPS数据，以武汉市160个街镇作为分析单元进行案例实证。结果表明：在公交影响下，城市疫情传播加速，多处出现“飞点传播”现象，整个城市感染者数量达70 446人。及时采取公交停运举措可使感染者数量降低18.64%~28.34%，实施越早，感染者数量减少越多。模拟COVID?19疫情中官方于2020年1月23日采取公交停运措施，其预测结果与官方公布疫情数据拟合优度达0.9675。相关研究成果和发现有助于揭示公共交通对城市内部疫情传播的影响，为复工复产后以及其他重大公共卫生事件发生时的风险判断及政策制定提供理论及决策依据。

关键词: 重大公共卫生事件, COVID?19, 城市公共交通, SEIR模型, 公交IC卡数据

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

中图分类号:

U121

邬少杰,孙健. 突发公共卫生事件下城市公交传播疫情模型[J]. 吉林大学学报(工学版), 2023, 53(1): 141-149.

Shao-jie WU,Jian SUN. Modeling urban bus transit spreading the epidemic under public health emergencies[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 141-149.

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日期	仿真感染人数	官方公布感染数	相对误差/%
2月12日	41 351	32 994	25.33
2月15日	44 737	39 462	13.37
2月18日	47 239	44 412	6.36
2月21日	48 853	45 660	6.99
2月24日	49 922	47 071	6.05
2月27日	50 619	48 137	5.16
3月01日	50 940	49 315	3.30
3月04日	51 112	49 671	2.90
3月07日	51 170	49 912	2.52
3月10日	51 186	49 978	2.42

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