A primer on using mathematics to understand COVID-19 dynamics: Modeling, analysis and simulations.
A primer on using mathematics to understand COVID-19 dynamics: Modeling, analysis and simulations.
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DOI：
10.1016/j.idm.2020.11.005
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发表时间：
2021
影响因子：
8.8
通讯作者：
Elbasha EH
中科院分区：
文献类型：
--
作者：
Gumel AB;Iboi EA;Ngonghala CN;Elbasha EH
2019年12月在武汉市出现的新型冠状病毒（COVID-19）大流行使世界各地的卫生系统不堪重负，经济陷入瘫痪。它成为自1918年西班牙流感大流行以来人类面临的最重要的公共卫生挑战。已经设计并使用了各种理论和经验方法来深入了解大流行的传播动态和控制。本研究为建立、分析和模拟新冠肺炎动态的数学模型提供了基础。具体而言，我们引入了具有同质和异质混合人群的简单区室kermack - mckendrick型流行病模型，这是一种用于评估假设的COVID-19疫苗的潜在人群水平影响的地方性模型。我们说明了如何将一些针对COVID-19的基本非药物干预措施纳入流行病模型。本文还简要介绍了用于研究COVID-19动态的其他类型模型，如基于agent的模型、网络模型和统计模型。提出了基本模型的可能扩展，以及与COVID-19动力学模型的制定和理论分析相关的开放式挑战。
The novel coronavirus (COVID-19) pandemic that emerged from Wuhan city in December 2019 overwhelmed health systems and paralyzed economies around the world. It became the most important public health challenge facing mankind since the 1918 Spanish flu pandemic. Various theoretical and empirical approaches have been designed and used to gain insight into the transmission dynamics and control of the pandemic. This study presents a primer for formulating, analysing and simulating mathematical models for understanding the dynamics of COVID-19. Specifically, we introduce simple compartmental, Kermack-McKendrick-type epidemic models with homogeneously- and heterogeneously-mixed populations, an endemic model for assessing the potential population-level impact of a hypothetical COVID-19 vaccine. We illustrate how some basic non-pharmaceutical interventions against COVID-19 can be incorporated into the epidemic model. A brief overview of other kinds of models that have been used to study the dynamics of COVID-19, such as agent-based, network and statistical models, is also presented. Possible extensions of the basic model, as well as open challenges associated with the formulation and theoretical analysis of models for COVID-19 dynamics, are suggested.
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DOI：
10.1016/j.jmaa.2012.04.077
发表时间：
2012-11-01
影响因子：
1.3
作者：
Gumel, A. B.
通讯作者：
Gumel, A. B.
影响因子：
1.9
作者：
Clancy, Damian;Pearce, Christopher J.
通讯作者：
Pearce, Christopher J.
影响因子：
4.3
作者：
Glasser, John;Feng, Zhilan;Castillo-Chavez, Carlos
通讯作者：
Castillo-Chavez, Carlos
影响因子：
8.8
作者：
Eikenberry, Steffen E.;Mancuso, Marina;Gumel, Abba B.
通讯作者：
Gumel, Abba B.
影响因子：
2.6
作者：
Feng, Zhilan
通讯作者：
Feng, Zhilan