South China Normal University, Guangzhou, China.
City University of Hong Kong, Hong Kong, Hong Kong.
JMIR Public Health Surveill. 2020 Jul 3;6(3):e18880. doi: 10.2196/18880.
The coronavirus disease (COVID-19) began to spread in mid-December 2019 from Wuhan, China, to most provinces in China and over 200 other countries through an active travel network. Limited by the ability of the country or city to perform tests, the officially reported number of confirmed cases is expected to be much smaller than the true number of infected cases.
This study aims to develop a new susceptible-exposed-infected-confirmed-removed (SEICR) model for predicting the spreading progression of COVID-19 with consideration of intercity travel and the difference between the number of confirmed cases and actual infected cases, and to apply the model to provide a realistic prediction for the United States and Japan under different scenarios of active intervention.
The model introduces a new state variable corresponding to the actual number of infected cases, integrates intercity travel data to track the movement of exposed and infected individuals among cities, and allows different levels of active intervention to be considered so that a realistic prediction of the number of infected individuals can be performed. Moreover, the model generates future progression profiles for different levels of intervention by setting the parameters relative to the values found from the data fitting.
By fitting the model with the data of the COVID-19 infection cases and the intercity travel data for Japan (January 15 to March 20, 2020) and the United States (February 20 to March 20, 2020), model parameters were found and then used to predict the pandemic progression in 47 regions of Japan and 50 states (plus a federal district) in the United States. The model revealed that, as of March 19, 2020, the number of infected individuals in Japan and the United States could be 20-fold and 5-fold as many as the number of confirmed cases, respectively. The results showed that, without tightening the implementation of active intervention, Japan and the United States will see about 6.55% and 18.2% of the population eventually infected, respectively, and with a drastic 10-fold elevated active intervention, the number of people eventually infected can be reduced by up to 95% in Japan and 70% in the United States.
The new SEICR model has revealed the effectiveness of active intervention for controlling the spread of COVID-19. Stepping up active intervention would be more effective for Japan, and raising the level of public vigilance in maintaining personal hygiene and social distancing is comparatively more important for the United States.
2019 年 12 月中旬，冠状病毒病（COVID-19）开始从中国武汉传播到中国大部分省份以及 200 多个其他国家，传播途径是活跃的旅行网络。由于国家或城市进行检测的能力有限，官方报告的确诊病例数预计远小于实际感染病例数。
本研究旨在开发一种新的易感-暴露-感染-确诊-清除（SEICR）模型，以预测 COVID-19 的传播进展，该模型考虑了城市间的旅行以及确诊病例数与实际感染病例数之间的差异，并应用该模型在不同的积极干预情景下对美国和日本进行现实预测。
该模型引入了一个新的状态变量，对应于实际感染病例数，整合了城市间旅行数据，以跟踪暴露和感染个体在城市之间的流动，并允许考虑不同水平的积极干预，从而对感染个体数量进行现实预测。此外，通过设定与数据拟合值相关的参数，该模型为不同干预水平生成未来进展情况。
通过对日本（2020 年 1 月 15 日至 3 月 20 日）和美国（2020 年 2 月 20 日至 3 月 20 日）的 COVID-19 感染病例和城市间旅行数据进行模型拟合，找到了模型参数，然后用于预测日本 47 个地区和美国 50 个州（外加一个联邦区）的大流行进展情况。模型显示，截至 2020 年 3 月 19 日，日本和美国的感染人数可能分别是确诊病例数的 20 倍和 5 倍。结果表明，如果不加强积极干预的实施，日本和美国将分别有 6.55%和 18.2%的人口最终感染，如果采取大幅提高 10 倍的积极干预，日本和美国最终感染人数最多可减少 95%和 70%。
新的 SEICR 模型揭示了积极干预对控制 COVID-19 传播的有效性。加强积极干预对日本更有效，而提高公众警惕性以保持个人卫生和社会距离对美国更为重要。
