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分数阶SEIR疫情模型及疫苗接种对COVID-19传播影响的研究

Study of Fractional Order SEIR Epidemic Model and Effect of Vaccination on the Spread of COVID-19.

作者信息

Paul Subrata, Mahata Animesh, Mukherjee Supriya, Roy Banamali, Salimi Mehdi, Ahmadian Ali

机构信息

Department of Mathematics, Arambagh Government Polytechnic, Arambagh, West Bengal India.

Mahadevnagar High School, Maheshtala, Kolkata, West Bengal 700141 India.

出版信息

Int J Appl Comput Math. 2022;8(5):237. doi: 10.1007/s40819-022-01411-4. Epub 2022 Aug 26.

DOI:10.1007/s40819-022-01411-4
PMID:36043055
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9412815/
Abstract

In this manuscript, a fractional order model with vaccination has been proposed. The positivity and boundedness of the solutions have been verified. The stability analysis of the model shows that the system is locally as well as globally asymptotically stable at disease-free equilibrium point when < 1 and at epidemic equilibrium when . It has been found that introduction of the vaccination parameter reduces the reproduction number . The parameters are identified using real-time data from COVID-19 cases in India. To numerically solve the model with vaccination, the Adam-Bashforth-Moulton technique is used. We employed MATLAB Software (Version 2018a) for graphical presentations and numerical simulations.. It has been observed that the SEIR model with fractional order derivatives of the dynamical variables is much more effective in studying the effect of vaccination than the integral model.

摘要

在本手稿中,提出了一个带有疫苗接种的分数阶模型。已验证了解的正性和有界性。模型的稳定性分析表明,当<1时系统在无病平衡点处局部和全局渐近稳定,当 时在流行平衡点处局部和全局渐近稳定。已发现引入疫苗接种参数 会降低再生数 。使用来自印度新冠肺炎病例的实时数据来识别参数。为了数值求解带有疫苗接种的模型,采用了亚当斯-巴什福思-莫尔顿技术。我们使用MATLAB软件(版本2018a)进行图形展示和数值模拟。已观察到,具有动态变量分数阶导数的SEIR模型在研究疫苗接种效果方面比积分模型更有效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81bf/9412815/3810e97726aa/40819_2022_1411_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81bf/9412815/32095760ccfa/40819_2022_1411_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81bf/9412815/5bcdb81a29f1/40819_2022_1411_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81bf/9412815/5ba5d21e65cc/40819_2022_1411_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81bf/9412815/1dbbc8947bf7/40819_2022_1411_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81bf/9412815/46c97c0e4a96/40819_2022_1411_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81bf/9412815/3810e97726aa/40819_2022_1411_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81bf/9412815/32095760ccfa/40819_2022_1411_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81bf/9412815/5bcdb81a29f1/40819_2022_1411_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81bf/9412815/5ba5d21e65cc/40819_2022_1411_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81bf/9412815/1dbbc8947bf7/40819_2022_1411_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81bf/9412815/46c97c0e4a96/40819_2022_1411_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81bf/9412815/3810e97726aa/40819_2022_1411_Fig6_HTML.jpg

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