Simple model of epidemic dynamics with memory effects

被引:18
|
作者
Bestehorn, Michael [1 ]
Michelitsch, Thomas M. [2 ]
Collet, Bernard A. [2 ]
Riascos, Alejandro P. [3 ]
Nowakowski, Andrzej F. [4 ]
机构
[1] Brandenburg Tech Univ Cottbus Senftenberg, Inst Phys, Erich Weinert Str 1, D-03046 Cottbus, Germany
[2] Sorbonne Univ, Inst Jean Rond dAlembert, CNRS, UMR 7190, 4 Pl Jussieu, F-75252 Paris 05, France
[3] Univ Nacl Autonoma Mexico, Inst Fis, Apartado Postal 20-364, Ciudad De Mexico 01000, Mexico
[4] Univ Sheffield, Dept Mech Engn, Mappin St, Sheffield 51 3JD, S Yorkshire, England
关键词
TIME RANDOM-WALKS; PERCOLATION; DIFFUSION;
D O I
10.1103/PhysRevE.105.024205
中图分类号
O35 [流体力学]; O53 [等离子体物理学];
学科分类号
070204 ; 080103 ; 080704 ;
摘要
We introduce a compartment model with memory for the dynamics of epidemic spreading in a constant population of individuals. Each individual is in one of the states S = susceptible, I = infected, or R = recovered (SIR model). In state R an individual is assumed to stay immune within a finite-time interval. In the first part, we introduce a random lifetime or duration of immunity which is drawn from a certain probability density function. Once the time of immunity is elapsed an individual makes an instantaneous transition to the susceptible state. By introducing a random duration of immunity a memory effect is introduced into the process which crucially determines the epidemic dynamics. In the second part, we investigate the influence of the memory effect on the space-time dynamics of the epidemic spreading by implementing this approach into computer simulations and employ a multiple random walker's model. If a susceptible walker meets an infectious one on the same site, then the susceptible one gets infected with a certain probability. The computer experiments allow us to identify relevant parameters for spread or extinction of an epidemic. In both parts, the finite duration of immunity causes persistent oscillations in the number of infected individuals with ongoing epidemic activity preventing the system from relaxation to a steady state solution. Such oscillatory behavior is supported by real-life observations and not the classical standard SIR model.
引用
收藏
页数:10
相关论文
共 50 条
  • [31] Fractional Dynamics of a Measles Epidemic Model
    Abboubakar, Hamadjam
    Fandio, Rubin
    Sofack, Brandon Satsa
    Fouda, Henri Paul Ekobena
    AXIOMS, 2022, 11 (08)
  • [32] A DIFFUSIVE EPIDEMIC MODEL WITH CRISSCROSS DYNAMICS
    FITZGIBBON, WE
    MARTIN, CB
    MORGAN, JJ
    JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS, 1994, 184 (03) : 399 - 414
  • [33] Random dynamics of an SIV epidemic model
    Elbaz, Islam M.
    Sohaly, M. A.
    El-Metwally, H.
    COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2024, 131
  • [34] Dynamics of an epidemic model with impact of toxins
    Saha, Sangeeta
    Samanta, G. P.
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2019, 527
  • [35] GLOBAL DYNAMICS OF A COUPLED EPIDEMIC MODEL
    Shu, Hongying
    Wang, Xiang-Sheng
    DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B, 2017, 22 (04): : 1575 - 1585
  • [36] Dynamics of an epidemic model with spatial diffusion
    Wang, Tao
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2014, 409 : 119 - 129
  • [37] Rich dynamics of an SIR epidemic model
    Pathak, S.
    Maiti, A.
    Samanta, G. P.
    NONLINEAR ANALYSIS-MODELLING AND CONTROL, 2010, 15 (01): : 71 - 81
  • [38] Stochastic dynamics of an SEIS epidemic model
    Bo Yang
    Advances in Difference Equations, 2016
  • [39] Dynamics of a delayed SEIQ epidemic model
    Wanjun Xia
    Soumen Kundu
    Sarit Maitra
    Advances in Difference Equations, 2018
  • [40] Stochastic dynamics of an SEIS epidemic model
    Yang, Bo
    ADVANCES IN DIFFERENCE EQUATIONS, 2016,