SEIR modeling of the COVID-19 and its dynamics
SEIR modeling of the COVID-19 and its dynamics
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DOI：
10.1007/s11071-020-05743-y
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发表时间：
2020-06-18
影响因子：
5.6
通讯作者：
Sun, Kehui
中科院分区：
文献类型：
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作者：
He, Shaobo;Peng, Yuexi;Sun, Kehui
本文针对COVID-19建立了一个SEIR流行病模型，该模型考虑了医院、检疫和外部输入等一般控制策略。以湖北省的数据为例，应用粒子群优化算法对系统参数进行了估计。我们发现，对于不同的场景，所提出的SEIR模型的参数是不同的。最后，利用该模型对湖北省的疫情进行了预测，结果表明该模型可以用于COVID-19疫情的预测。此外，通过引入季节性和参数的随机性，发现系统具有包含混沌的非线性动力学特性。最后，我们讨论了基于所提出的模型的结构和参数的COVID-19的控制策略。
In this paper, a SEIR epidemic model for the COVID-19 is built according to some general control strategies, such as hospital, quarantine and external input. Based on the data of Hubei province, the particle swarm optimization (PSO) algorithm is applied to estimate the parameters of the system. We found that the parameters of the proposed SEIR model are different for different scenarios. Then, the model is employed to show the evolution of the epidemic in Hubei province, which shows that it can be used to forecast COVID-19 epidemic situation. Moreover, by introducing the seasonality and stochastic infection the parameters, nonlinear dynamics including chaos are found in the system. Finally, we discussed the control strategies of the COVID-19 based on the structure and parameters of the proposed model.