Nonlinear dynamics modeling and epidemic forecast of COVID-19

被引：0
|
作者：
Yu Z.-H. [1 ]
Huang S.-G. [1 ]
Lu S. [1 ]
Gao H.-X. [1 ]
机构：
[1] College of Computer Science & Technology, Xi'an University of Science and Technology, Xi'an
来源：
Kongzhi yu Juece/Control and Decision | 2023年 / 38卷 / 03期
关键词：
bifurcations; COVID-19; epidemic model; equilibrium; non-linear dynamics; parametric estimation;
D O I：
10.13195/j.kzyjc.2021.1092
中图分类号：
学科分类号：
摘要：
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 named SLIR(susceptible-low-risk-infected-recovered), 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 proposed model can effectively simulate the spreading of COVID-19 and help governments choose appropriate prevention and control measures. Copyright ©2023 Control and Decision.
引用
收藏
页码：699 / 705
页数：6
相关论文
共 27 条
  • [1] Arazi R, Feigel A., Discontinuous transitions of social distancing in the SIR model, Physica A: Statistical Mechanics and Its Applications, 566, (2021)
  • [2] Kiouach D, Sabbar Y., Global dynamics analysis of a stochastic SIRS epidemic model with vertical transmission and different periods of immunity, International Journal of Dynamical Systems and Differential Equations, 10, 5, pp. 468-491, (2020)
  • [3] Alrabaiah H, Arfan M, Shah K, Et al., A comparative study of spreading of novel corona virus disease by ussing fractional order modified SEIR model, Alexandria Engineering Journal, 60, 1, pp. 573-585, (2021)
  • [4] Ram V, Schaposnik L P., A modified age-structured SIR model for COVID-19 type viruses, Scientific Reports, 11, 1, (2021)
  • [5] Cooper I, Mondal A, Antonopoulos C G., A SIR model assumption for the spread of COVID-19 in different communities, Chaos, Solitons & Fractals, 139, pp. 110057-110076, (2020)
  • [6] Jia W P, Han K, Song Y, Et al., Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China, Frontiers in Medicine, 7, pp. 169-186, (2020)
  • [7] Lymperopoulos I N., #stayhome to contain Covid-19: Neuro-SIR-Neurodynamical epidemic modeling of infection patterns in social networks, Expert Systems With Applications, 165, (2021)
  • [8] Lux T., The social dynamics of COVID-19, Physica A, 567, (2021)
  • [9] Jahanshahi H, Munoz-Pacheco J M, Bekiros S, Et al., A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19, Chaos, Solitons & Fractals, 143, (2021)
  • [10] Sun H C, Liu X F, Xu X K, Et al., Analysis of COVID-19 spreading and prevention strategy in schools based on continuous infection model, Acta Physica Sinica, 69, 24, pp. 74-83, (2020)
123