新冠肺炎传播动力学建模及预测
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作者单位:

西安科技大学

作者简介:

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中图分类号:

TP399

基金项目:

国家自然科学基金


Nonlinear Dynamics Modeling and Epidemic Forecast of COVID-19
Author:
Affiliation:

Xi''an University of Science and Technology

Fund Project:

61873277

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    摘要:

    为了深入研究新冠肺炎传播趋势和传播风险,根据新冠肺炎的传播特点,考虑政府管控和个人防护等措施,在经典传染病 SIR 模型的基础上,引入低风险群体,提出一种新的新冠肺炎传播动力学模型 SLIR,并对模型的平衡点、稳定性和分岔等复杂动力学行为进行分析,揭示新冠肺炎传播机理。为了提高该模型的疫情预测精度,以美国新冠肺炎的真实数据为基础,使用最小二乘法对模型参数进行分段估计。最后利用该模型对美国新冠肺炎进行预测和分析,仿真结果表明,相比于传统 SIR 模型,该模型能较好地对美国疫情发展趋势做出预测,官方公布的实际数据也进一步验证了模型的有效性。 SLIR 模型可以有效仿真新冠肺炎传播,并为政府选择合适的防控措施提供技术支撑。

    Abstract:

    To study the spreading trend and risk of COVID-19, according to the characteristics of COVID-19, this paper proposes a new transmission dynamic model SLIR based on the classic SIR model by considering government control and personal protection measures. The equilibria, stability and bifurcation of the model are analyzed to reveal the propagation mechanism of COVID-19. In order to improve the prediction accuracy of the model, the least square method is employed to estimate the model parameters based on the real data of COVID-19 in the United States. Finally, the model is used to predict and analyze COVID-19 in the United States. The simulation results show that compared with the traditional SIR model, this model can better predict the spreading trend of COVID-19 in the United States, and the actual official data has further verified its effectiveness. The model proposed in this paper can effectively simulate the spreading of COVID-19 and help governments choose appropriate prevention and control measures.

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历史
  • 收稿日期:2021-06-23
  • 最后修改日期:2021-12-28
  • 录用日期:2021-12-30
  • 在线发布日期: 2022-02-01
  • 出版日期: