论文标题
COVID-19的流行病学模型的非线性动态分析，包括公共行为和政府行动
Nonlinear dynamic analysis of an epidemiological model for COVID-19 including public behavior and government action
论文作者
论文摘要
本文与目前破坏了地球的Covid-19大流行有关的非线性建模和分析。有两个目标：要达成一个适当的模型来忠实地捕获收集的数据，并将其作为探索非线性行为的基础。我们使用非线性SEIR（易感性，暴露，传染性和删除）传输模型，并增加了行为和政府政策动态。我们开发了一种遗传算法技术，可以识别使用韩国的COVID19数据的关键模型参数。分析稳定性，分叉和动态行为。参数分析揭示了发生持续流行均衡的条件。这项工作指出了非线性动态分析在大流行建模中的价值，并证明了社会和政府行为对疾病动态的巨大影响。
This paper is concerned with nonlinear modeling and analysis of the COVID-19 pandemic currently ravaging the planet. There are two objectives: to arrive at an appropriate model that captures the collected data faithfully, and to use that as a basis to explore the nonlinear behavior. We use a nonlinear SEIR (Susceptible, Exposed, Infectious & Removed) transmission model with added behavioral and government policy dynamics. We develop a genetic algorithm technique to identify key model parameters employing COVID19 data from South Korea. Stability, bifurcations and dynamic behavior are analyzed. Parametric analysis reveals conditions for sustained epidemic equilibria to occur. This work points to the value of nonlinear dynamic analysis in pandemic modeling and demonstrates the dramatic influence of social and government behavior on disease dynamics.
