PDF(Pubmed)
Abstract：
UNASSIGNED: The development of an epidemic always exhibits multiwave oscillation owing to various anthropogenic sources of transmission. Particularly in populated areas, the large-scaled human mobility led to the transmission of the virus faster and more complex. The accurate prediction of the spread of infectious diseases remains a problem. To solve this problem, we propose a new method called the multi-source dynamic ensemble prediction (MDEP) method that incorporates a modified susceptible-exposed-infected-removed (SEIR) model to improve the accuracy of the prediction result.
UNASSIGNED: The modified SEIR model is based on the compartment model, which is suitable for local-scale and confined spaces, where human mobility on a large scale is not considered. Moreover, compartmental models cannot be used to predict multiwave epidemics. The proposed MDEP method can remedy defects in the compartment model. In this study, multi-source prediction was made on the development of coronavirus disease 2019 (COVID-19) and dynamically assembled to obtain the final integrated result. We used the real epidemic data of COVID-19 in three cities in China: Beijing, Lanzhou, and Beihai. Epidemiological data were collected from 17 April, 2022 to 12 August, 2022.
UNASSIGNED: Compared to the one-wave modified SEIR model, the MDEP method can depict the multiwave development of COVID-19. The MDEP method was applied to predict the number of cumulative cases of recent COVID-19 outbreaks in the aforementioned cities in China. The average accuracy rates in Beijing, Lanzhou, and Beihai were 89.15%, 91.74%, and 94.97%, respectively.
UNASSIGNED: The MDEP method improved the prediction accuracy of COVID-19. With further application to other infectious diseases, the MDEP method will provide accurate predictions of infectious diseases and aid governments make appropriate directives.
UNASSIGNED: The modified SEIR model is based on the compartment model, which is suitable for local-scale and confined spaces, where human mobility on a large scale is not considered. Moreover, compartmental models cannot be used to predict multiwave epidemics. The proposed MDEP method can remedy defects in the compartment model. In this study, multi-source prediction was made on the development of coronavirus disease 2019 (COVID-19) and dynamically assembled to obtain the final integrated result. We used the real epidemic data of COVID-19 in three cities in China: Beijing, Lanzhou, and Beihai. Epidemiological data were collected from 17 April, 2022 to 12 August, 2022.
UNASSIGNED: Compared to the one-wave modified SEIR model, the MDEP method can depict the multiwave development of COVID-19. The MDEP method was applied to predict the number of cumulative cases of recent COVID-19 outbreaks in the aforementioned cities in China. The average accuracy rates in Beijing, Lanzhou, and Beihai were 89.15%, 91.74%, and 94.97%, respectively.
UNASSIGNED: The MDEP method improved the prediction accuracy of COVID-19. With further application to other infectious diseases, the MDEP method will provide accurate predictions of infectious diseases and aid governments make appropriate directives.
摘要：
■由于各种人为传播源，流行病的发展总是表现出多波振荡。特别是在人口稠密的地区，人类的大规模流动导致病毒的传播更快、更复杂。准确预测传染病的传播仍然是一个问题。为了解决这个问题,我们提出了一种称为多源动态集成预测（MDEP）方法的新方法，该方法结合了改进的易感暴露感染去除（SEIR）模型，以提高预测结果的准确性。
■修改后的SEIR模型基于隔室模型，适用于局部尺度和密闭空间，没有考虑大规模的人类流动性。此外，隔室模型不能用于预测多波流行病。提出的MDEP方法可以弥补隔室模型中的缺陷。在这项研究中,对2019年冠状病毒病（COVID-19）的发展进行多源预测，并动态组装以获得最终的综合结果。我们使用了中国三个城市的COVID-19的真实疫情数据：北京，兰州和北海。从4月17日开始收集流行病学数据，2022年8月12日，2022年。
■与单波修改的SEIR模型相比，MDEP方法可以描述COVID-19的多波发展。MDEP方法用于预测中国上述城市近期COVID-19暴发的累积病例数。北京的平均准确率，兰州北海占89.15%，91.74%,94.97%，分别。
■MDEP方法提高了COVID-19的预测精度。随着对其他传染病的进一步应用，MDEP方法将提供对传染病的准确预测，并帮助政府制定适当的指令。
■修改后的SEIR模型基于隔室模型，适用于局部尺度和密闭空间，没有考虑大规模的人类流动性。此外，隔室模型不能用于预测多波流行病。提出的MDEP方法可以弥补隔室模型中的缺陷。在这项研究中,对2019年冠状病毒病（COVID-19）的发展进行多源预测，并动态组装以获得最终的综合结果。我们使用了中国三个城市的COVID-19的真实疫情数据：北京，兰州和北海。从4月17日开始收集流行病学数据，2022年8月12日，2022年。
■与单波修改的SEIR模型相比，MDEP方法可以描述COVID-19的多波发展。MDEP方法用于预测中国上述城市近期COVID-19暴发的累积病例数。北京的平均准确率，兰州北海占89.15%，91.74%,94.97%，分别。
■MDEP方法提高了COVID-19的预测精度。随着对其他传染病的进一步应用，MDEP方法将提供对传染病的准确预测，并帮助政府制定适当的指令。
