Xiang Wang, Chen Li, Yan Xuedong, Wang Bin, Liu Xiaobing
Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science & Technology, Changsha 410114, China.
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China.
Cities. 2023 Apr;135:104238. doi: 10.1016/j.cities.2023.104238. Epub 2023 Feb 13.
With the spatial structure of urban agglomerations, well-developed transportation networks and close economic ties can increase the risk of intercity transmission of infectious diseases. To reveal the epidemic transmission mechanism in urban agglomerations and to explore the effectiveness of traffic control measures, this study proposes an Urban-Agglomeration-based Epidemic and Mobility Model (UAEMM) based on the reality of urban transportation networks and population mobility factors. Since the model considers the urban population inflow, along with the active intracity population, it can be used to estimate the composition of urban cases. The model was applied to the Chang-Zhu-Tan urban agglomeration, and the results show that the model can better simulate the transmission process of the urban agglomeration for a certain scale of epidemic. The number of cases within the urban agglomeration is higher than the number of cases imported into the urban agglomeration from external cities. The composition of cases in the core cities of the urban agglomeration changes with the adjustment of prevention and control measures. In contrast, the number of cases imported into the secondary cities is consistently greater than the number of cases transmitted within the cities. A traffic control measures discount factor is introduced to simulate the development of the epidemic in the urban agglomeration under the traffic control measures of the first-level response to major public health emergency, traffic blockades in infected areas, and public transportation shutdowns. If none of those traffic control measures had been taken after the outbreak of COVID-19, the number of cases in the urban agglomeration would theoretically have increased to 3879, which is 11.61 times the actual number of cases that occurred. If only one traffic control measure had been used alone, each of the three measures would have reduced the number of cases in the urban agglomeration to 30.19 %-57.44 % of the theoretical values of infection cases, with the best blocking effect coming from the first-level response to major public health emergency. Traffic control measures have a significant effect in interrupting the spread of COVID-19 in urban agglomerations. The methodology and main findings presented in this paper are of general interest and can also be used in studies in other countries for similar purposes to help understand the spread of COVID-19 in urban agglomerations.
随着城市群的空间结构、发达的交通网络以及紧密的经济联系，传染病城际传播的风险会增加。为揭示城市群中的疫情传播机制并探索交通管控措施的有效性，本研究基于城市交通网络和人口流动因素的实际情况，提出了一种基于城市群的疫情与流动性模型（UAEMM）。由于该模型考虑了城市人口流入以及活跃的城市内部人口，它可用于估计城市病例的构成。该模型应用于长株潭城市群，结果表明，对于一定规模的疫情，该模型能够较好地模拟城市群的传播过程。城市群内的病例数高于从外部城市输入到城市群的病例数。城市群核心城市的病例构成随防控措施的调整而变化。相比之下，二级城市输入的病例数始终大于城市内部传播的病例数。引入交通管控措施折扣因子，以模拟在重大突发公共卫生事件一级响应、感染区域交通封锁以及公共交通停运等交通管控措施下城市群内疫情的发展情况。如果在新冠疫情爆发后未采取这些交通管控措施，城市群内的病例数理论上会增至3879例，这是实际发生病例数的11.61倍。如果仅单独使用一项交通管控措施，三项措施中的每一项都会使城市群内的病例数降至感染病例理论值的30.19% - 57.44%，其中一级响应重大突发公共卫生事件的阻断效果最佳。交通管控措施对阻断新冠疫情在城市群中的传播有显著效果。本文提出的方法和主要研究结果具有普遍意义，也可用于其他国家类似目的的研究，以帮助了解新冠疫情在城市群中的传播情况。
