Global stability and optimal control of an SIRS epidemic model on heterogeneous networks

Chen, Lijuan; Sun, Jitao

doi:10.1016/j.physa.2014.05.034

Cited by 81 publications

(34 citation statements)

References 21 publications

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“…Stability analysis is an important tool to understand the spreading dynamics of epidemic models on complex networks. Recently, there have been many studies concerning the stability and asymptotic behavior of epidemic models on complex networks [15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Global stability of an SIS epidemic model with feedback mechanism on networks

Wei

Zhou

2018

Adv Differ Equ

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We study the global stability of endemic equilibrium of an SIS epidemic model with feedback mechanism on networks. The model was proposed by J. Zhang and J. Sun (Physica A 394:24-32, 2014), who obtained the local asymptotic stability of endemic equilibrium. Our main purpose is to show that if the feedback parameter is sufficiently large or if the basic reproductive number belongs to the interval (1, 2], then the endemic equilibrium is globally asymptotically stable. We also present numerical simulations to illustrate the theoretical results.

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Section: Introductionmentioning

confidence: 99%

Global stability of an SIS epidemic model with feedback mechanism on networks

Wei

Zhou

2018

Adv Differ Equ

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“…As we know, the final goals of studying emotion contagion is to eradicate negative emotions from the human population in emergencies. Infectious disease control studies-inspired, a few of control strategies may be used to inhibit emotion contagion, including vaccination, quarantine and treatment [27,28]. However, it incurs costs for authorities to employ the above strategies.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Computational models and optimal control strategies for emotion contagion in the human population in emergencies

Wang

Zhang

Lin

et al. 2016

Knowledge-Based Systems

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“…The parameters a, b and c ∈ R + are the related constant transition rates. The SIRS model differs from SIS or SIR model because of the short-term immunity considered in [15]. The above SIRS model can be derived by utilizing Kurtz's theorem while the population is large enough [16].…”

Section: B the Sirs Modelmentioning

confidence: 99%

Node-based SIRS model on heterogeneous networks: Analysis and control

Liu

Buss

2016

2016 American Control Conference (ACC)

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Abstract-In this paper, a node-base susceptible-infectedrecovered-susceptible (SIRS) model on heterogeneous networks is proposed. The condition for global exponential stability of the origin in the disease-free case is obtained via Lyapunov theory. Furthermore, aiming at regulating the probabilities of being infected and recovered to desired values, the controlled recovering rates are designed. Taking practical implementation into consideration, an alternative approach is provided to calculate the feasible control inputs. Finally, several numerical simulations validate our results.

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Global stability and optimal control of an SIRS epidemic model on heterogeneous networks

Cited by 81 publications

References 21 publications

Global stability of an SIS epidemic model with feedback mechanism on networks

Global stability of an SIS epidemic model with feedback mechanism on networks

Computational models and optimal control strategies for emotion contagion in the human population in emergencies

Node-based SIRS model on heterogeneous networks: Analysis and control

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