Zhou Fang, Hou Fang, Wang Jiangtao, Ma Qiaoyun, Luo Lanfen
College of Information and Management Science, Henan Agricultural University, Zhengzhou, China.
Front Public Health. 2024 Dec 12;12:1454450. doi: 10.3389/fpubh.2024.1454450. eCollection 2024.
A well-connected transportation network unites localities but also accelerates the transmission of infectious diseases. Subways-an important aspect of daily travel in big cities-are high-risk sites for the transmission of urban epidemics. Intensive research examining the transmission mechanisms of infectious diseases in subways is necessary to ascertain the risk of disease transmission encountered by commuters.
In this study, we improve the susceptible-exposed-infected-recovered (SEIR) model and propose the susceptible-exposed-infected-asymptomatic infected (SEIA) model. First, we added asymptomatic patients to the improved model as a parameter to explore the role of asymptomatic patients in the transmission of infectious diseases in a subway. The numbers of boarding and alighting passengers were added to the model as two time-varying parameters to simulate the exchange of passengers at each station.
The improved model could simulate the transmission of infectious diseases in subways and identify the key factors of transmission. We then produced an example of the transmission of coronavirus disease (COVID-19) in a subway using real subway passenger data substituted into the model for the calculations.
We ascertained that the number of exposed people continuously increased with the operation of the subway. Asymptomatic patients had a greater impact on the transmission of infectious diseases than infected people in the course of transmission. The SEIA model constructed in this study accurately determined the spread of infectious diseases in a subway and may also be applicable to studies on the transmission of infectious diseases in other urban public transport systems.
一个连接良好的交通网络将各地连接起来,但也加速了传染病的传播。地铁作为大城市日常出行的一个重要方面,是城市疫情传播的高风险场所。有必要开展深入研究以探究地铁中传染病的传播机制,从而确定通勤者面临的疾病传播风险。
在本研究中,我们改进了易感-暴露-感染-康复(SEIR)模型,并提出了易感-暴露-感染-无症状感染(SEIA)模型。首先,我们将无症状患者作为一个参数添加到改进后的模型中,以探究无症状患者在地铁传染病传播中的作用。将上下车乘客数量作为两个随时间变化的参数添加到模型中,以模拟各站点的乘客交换情况。
改进后的模型能够模拟地铁中传染病的传播,并识别传播的关键因素。然后,我们利用代入模型进行计算的真实地铁乘客数据,给出了一个地铁中冠状病毒病(COVID-19)传播的示例。
我们确定随着地铁运营,暴露人群数量持续增加。在传播过程中,无症状患者对传染病传播的影响比感染者更大。本研究构建的SEIA模型准确地确定了地铁中传染病的传播情况,也可能适用于其他城市公共交通系统中传染病传播的研究。