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General Model for COVID-19 Spreading With Consideration of Intercity Migration, Insufficient Testing, and Active Intervention: Modeling Study of Pandemic Progression in Japan and the United States

机译:考虑到城市间迁移,测试不足和积极干预的综合模型 - 展开:日本与美国流行进程的建模研究

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Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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.
机译:背景:冠状病毒疾病(Covid-19)开始于2019年12月中旬从中国武汉到中国大多数省份和200多个其他国家通过积极的旅行网络。受到国家或城市进行测试的能力的限制,正式报告的确诊案件数量预计远小于受感染病例的真实数量。目的:本研究旨在开发一种新的易感暴露感染的已删除(SEICR)模型,用于预测Covid-19的扩散进展,考虑到城市间行程以及确诊病例数与实际感染病例之间的差异,并申请该模型为在有效干预的不同情景下为美国和日本提供了一个现实预测。方法:该模型介绍了与实际受感染病例相对应的新状态变量,整合城市间暴露和受感染的个体的运动,并允许考虑不同程度的积极干预,以便逼真的预测可以进行感染的个体的数量。此外,该模型通过将参数相对于从数据拟合中找到的值设置参数来生成不同级别的干预级别。结果:通过将模型与Covid-19感染案例的数据和日本(1月15日至3月20日)和美国(2月20日至3月20日)与日本(1月15日至3月20日)拟合,发现了模型参数然后用于预测美国47个地区的大流行进展和美国50个州(加上联邦区)。该模型透露,截至2020年3月19日,日本和美国的受感染个体的人数分别可以分别为20倍,5倍和5倍,分别为确诊病例的数量。结果表明,在不收紧积极干预的情况下,日本和美国将分别看到6.55%和18.2%的人口最终感染,并具有激烈的10倍的积极干预,最终的人数在日本的感染率可降低高达95%,美国70%。结论:新的SEICR模型揭示了控制Covid-19传播的积极干预的有效性。加紧积极干预将对日本更加有效,并提高维持个人卫生和社会疏散方面的公众警惕水平对美国相对更重要。

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