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SIR模型的分析特征及其在COVID-19中的应用。

Analytical features of the SIR model and their applications to COVID-19.

作者信息

Kudryashov Nikolay A, Chmykhov Mikhail A, Vigdorowitsch Michael

机构信息

Department of Applied Mathematics, National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 31 Kashirskoe Shosse, Moscow 115409, Russian Federation.

Angara GmbH, In der Steele 2, Düsseldorf 40599, Germany.

出版信息

Appl Math Model. 2021 Feb;90:466-473. doi: 10.1016/j.apm.2020.08.057. Epub 2020 Sep 28.

Abstract

A classic two-parameter epidemiological SIR-model of the coronavirus propagation is considered. The first integrals of the system of non-linear equations are obtained. The Painlevé test shows that the system of equations is not integrable in the general case. However, the general solution is obtained in quadrature as an inverse time-function. Using the first integrals of the system of equations, analytical dependencies for the number of infected patients () and that of recovered patients () on the number of susceptible to infection () are obtained. A particular attention is paid to interrelation of () and () both depending on /, where is the contact rate in the community and is the intensity of recovery/decease of patients. It is demonstrated that the data on particular morbidity waves in Hubei (China), Italy, Austria, South Korea, Moscow (Russia) as well some Australian territories are satisfactorily described by the expressions obtained for (). The variability of parameter having been traditionally considered as a static population size is discussed.

摘要

考虑了冠状病毒传播的经典双参数流行病学SIR模型。得到了非线性方程组的首次积分。Painlevé检验表明,该方程组在一般情况下不可积。然而,一般解可以通过求积法作为逆时间函数得到。利用方程组的首次积分,得到了感染患者数量()和康复患者数量()关于易感感染人数()的解析依赖关系。特别关注了()和()之间的相互关系,它们都依赖于/,其中是社区中的接触率,是患者康复/死亡的强度。结果表明,中国湖北、意大利、奥地利、韩国、俄罗斯莫斯科以及澳大利亚一些地区的特定发病波数据可以用()的表达式得到令人满意的描述。讨论了传统上被视为静态人口规模的参数的变异性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/031d/7521893/2c694a6f5517/gr1_lrg.jpg

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