论文标题
数据驱动的Covid-19的SEIR模型的最佳控制
Data-driven optimal control of a SEIR model for COVID-19
论文作者
论文摘要
我们提出了一种数据驱动的最佳控制方法，该方法将报告的部分数据与COVID-19的流行动力学相结合。我们使用基本的易感暴露感染（SEIR）模型，模型参数是及时的，并且从数据中学到了。这种方法可以预测相对较短的时间爆发的演变，并提供对流行病的预定控制。我们基于与最佳控制理论相关的广义泛素原理提供有效的数值算法。数值实验证明了所提出的模型的有效性能及其数值近似。
We present a data-driven optimal control approach which integrates the reported partial data with the epidemic dynamics for COVID-19. We use a basic Susceptible-Exposed-Infectious-Recovered (SEIR) model, the model parameters are time-varying and learned from the data. This approach serves to forecast the evolution of the outbreak over a relatively short time period and provide scheduled controls of the epidemic. We provide efficient numerical algorithms based on a generalized Pontryagin Maximum Principle associated with the optimal control theory. Numerical experiments demonstrate the effective performance of the proposed model and its numerical approximations.
