新冠肺炎大流行研究的拓扑方法:用于理解和帮助决策的定性模型

J. Rodríguez-Millán
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引用次数: 0

摘要

数学模型要么是战略性的、简化的,用于研究全局的定性性质,要么是战术性的、详细的,适合于对现实进行精细的定量调整。当复杂系统与其介质相互作用或经历参数扰动时,它们可能会发生顺序变化,从而使定性和定量研究变得困难。流行病学过程可以区分拓扑变化和动态变化,并确定它们之间的优先次序。在本文中,我们以这种方式处理COVID-19,将诱导系统中顺序变化的拓扑转换与动态转换本身分开。然后,我们开发了一个视觉隐喻,一系列图像来支持定格运动,允许区分阶段,识别和分类场景,并提出行动建议,以提高对大流行的理解和控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A topological approach to the study of COVID-19 pandemic: qualitative models for understanding and helping taking decisions
Mathematical models are either strategic, simplified, to study global qualitative properties, or tactic, detailed, appropriate for fine quantitative adjustment to reality. When complex systems interact with their medium or undergo parameter perturbations, they can suffer changes of order making qualitative and quantitative studies difficult. Epidemiological processes allow distinguishing between topological and dynamical alterations, and establishing precedence among them. In this essay we approach COVID-19 this way, to separate topological transformations inducing changes of order in the system, from dynamic transformations themselves. We then develop a visual metaphor, a sequence of images to support a stop-motion, allowing distinguishing the stages, identifying and classifying sceneries, and suggest actions to improve the understanding and control, of the pandemic.
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