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基于广义分数阶SEIR模型的美国新冠肺炎疫情趋势预测分析

Forecast analysis of the epidemics trend of COVID-19 in the USA by a generalized fractional-order SEIR model.

作者信息

Xu Conghui, Yu Yongguang, Chen YangQuan, Lu Zhenzhen

机构信息

Department of Mathematics ,School of Science, Beijing Jiaotong University, Beijing, 100044 China.

Mechatronics, Embedded Systems and Automation Lab, University of California, Merced, Merced, CA 95343 USA.

出版信息

Nonlinear Dyn. 2020;101(3):1621-1634. doi: 10.1007/s11071-020-05946-3. Epub 2020 Sep 14.

DOI:10.1007/s11071-020-05946-3
PMID:32952299
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7487266/
Abstract

In this paper, a generalized fractional-order SEIR model is proposed, denoted by SEIQRP model, which divided the population into susceptible, exposed, infectious, quarantined, recovered and insusceptible individuals and has a basic guiding significance for the prediction of the possible outbreak of infectious diseases like the coronavirus disease in 2019 (COVID-19) and other insect diseases in the future. Firstly, some qualitative properties of the model are analyzed. The basic reproduction number is derived. When , the disease-free equilibrium point is unique and locally asymptotically stable. When , the endemic equilibrium point is also unique. Furthermore, some conditions are established to ensure the local asymptotic stability of disease-free and endemic equilibrium points. The trend of COVID-19 spread in the USA is predicted. Considering the influence of the individual behavior and government mitigation measurement, a modified SEIQRP model is proposed, defined as SEIQRPD model, which is divided the population into susceptible, exposed, infectious, quarantined, recovered, insusceptible and dead individuals. According to the real data of the USA, it is found that our improved model has a better prediction ability for the epidemic trend in the next two weeks. Hence, the epidemic trend of the USA in the next two weeks is investigated, and the peak of isolated cases is predicted. The modified SEIQRP model successfully capture the development process of COVID-19, which provides an important reference for understanding the trend of the outbreak.

摘要

本文提出了一种广义分数阶SEIR模型,记为SEIQRP模型,该模型将人群分为易感者、潜伏者、感染者、隔离者、康复者和不易感者,对预测2019年冠状病毒病(COVID-19)等传染病未来可能的爆发具有重要指导意义。首先,分析了该模型的一些定性性质。推导了基本再生数。当 时,无病平衡点是唯一的且局部渐近稳定。当 时,地方病平衡点也是唯一的。此外,建立了一些条件以确保无病平衡点和地方病平衡点的局部渐近稳定性。预测了COVID-19在美国的传播趋势。考虑到个体行为和政府缓解措施的影响,提出了一种改进的SEIQRP模型,记为SEIQRPD模型,该模型将人群分为易感者、潜伏者、感染者、隔离者、康复者、不易感者和死亡者。根据美国的实际数据,发现我们改进后的模型对未来两周的疫情趋势具有更好的预测能力。因此,研究了美国未来两周的疫情趋势,并预测了隔离病例的峰值。改进后的SEIQRP模型成功地捕捉了COVID-19的发展过程,为理解疫情爆发趋势提供了重要参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534d/7487266/fd37a6bf6096/11071_2020_5946_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534d/7487266/2a02cf4e1a0f/11071_2020_5946_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534d/7487266/9ea1ca4150c4/11071_2020_5946_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534d/7487266/41cf14fd1727/11071_2020_5946_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534d/7487266/d30e1b74adaf/11071_2020_5946_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534d/7487266/fd37a6bf6096/11071_2020_5946_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534d/7487266/2a02cf4e1a0f/11071_2020_5946_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534d/7487266/9ea1ca4150c4/11071_2020_5946_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534d/7487266/41cf14fd1727/11071_2020_5946_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534d/7487266/d30e1b74adaf/11071_2020_5946_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534d/7487266/fd37a6bf6096/11071_2020_5946_Fig5_HTML.jpg

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