Dynamics Modeling and Analysis of COVID-19

YU Zhenhua; HUANG Shange; YANG Bo; GAO Hongxia; LU Si

doi:10.7652/xjtuxb202205005

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Dynamics Modeling and Analysis of COVID-19

更新时间：2025-07-09

- Dynamics Modeling and Analysis of COVID-19
- Vol. 56, Issue 5, Pages: 43-53(2022)
- 作者机构：
  
  1. 西安科技大学计算机科学与技术学院,西安,710054
  2. 西安科技大学智能系统安全与控制研究所
  3. 西安交通大学自动化科学与工程学院
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202205005
  CLC： TP399
- Online First：10 May 2022，
  
  Published：2022
- 稿件说明：
移动端阅览
YU Zhenhua, HUANG Shange, YANG Bo, et al. Dynamics Modeling and Analysis of COVID-19[J]. 2022, 56(5): 43-53.
DOI：

YU Zhenhua, HUANG Shange, YANG Bo, et al. Dynamics Modeling and Analysis of COVID-19[J]. 2022, 56(5): 43-53. DOI： 10.7652/xjtuxb202205005.

摘要

为研究新型冠状病毒肺炎(简称新冠肺炎)传播机理和传播风险

预测疫情发展趋势

对政府制定相关疫情防控政策提供帮助

提出了一种新的新冠肺炎传播非线性动力学模型(SLEIR)。该模型考虑到疫情中采取保护措施的人群

将其作为低危群体加入到模型中; 通过对模型的基本再生数、平衡点、稳定性和分岔等进行分析

揭示新冠肺炎传播机理; 利用印度新冠肺炎真实数据对模型参数和部分状态初值进行最小二乘拟合

根据拟合的参数对印度疫情发展趋势做出预测。该模型对印度3～4月、4～5月两阶段疫情预测平均相对误差分别为4.107%和2.805%

对于印度10月最新的疫情

预测平均相对误差为3.266%

预测结果表明SLEIR模型具有较好的预测效果。与传统SEIR模型相比

该模型能适应印度疫情复杂的变化趋势

且具有更高的预测精度

可以为政府选择合适的防控措施提供技术支撑。

Abstract

To study the spreading mechanism and risk and predict the spreading trend of COVID-19

and provide supports for the government to formulate prevention and control policies

a new nonlinear dynamic model

i.e.

the susceptible-low risk-exposed-infectious-removed(SLEIR)model is proposed

and the population with protection measures is regarded as a low-risk group and included into this model. The basic reproduction number

equilibrium

stability and bifurcation are analyzed to reveal the spreading mechanism

and the data on COVID-19 in India is used for least-squares fitting of model parameters and some initial values. The fitted parameters are used to predict the spreading trend in India. Predicting results show that the average relative errors of the epidemic prediction are 4.107% and 2.805% from March to April and from April to May

respectively. For the latest epidemic prediction of India in October

the average relative error is 3.266%. These simulations indicate that the proposed model can accurately predict COVID-19 spreading in India

is more suitable for analyzing its spreading in India with higher prediction accuracy compared with the traditional SEIR model

and can provide technical support for the government of India to select prevention and control measures.

关键词

Keywords

references

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