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.

本文根据一些通用控制策略（如医院、隔离和外部输入）构建了 COVID-19 的 SEIR 流行病模型。基于湖北省的数据，采用粒子群优化(PSO)算法对系统参数进行估计。我们发现所提出的 SEIR 模型的参数对于不同的场景是不同的。然后，利用该模型展示了湖北省疫情的演变，表明该模型可以用于预测COVID-19疫情。此外，通过引入季节性和随机感染参数，在系统中发现了包括混沌在内的非线性动力学。最后，我们根据所提出模型的结构和参数讨论了 COVID-19 的控制策略。