Dynamics and optimal control of a stochastic coronavirus (COVID-19) epidemic model with diffusion

Li, Yuxi; Wei, Zhouchao

doi:10.1007/s11071-021-06998-9

Cited by 19 publications

(8 citation statements)

References 42 publications

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“…On the other hand, natural systems can generally be affected by environmental noise such as white noise [26][27][28] and Lévy noise [29][30][31]. Then, random fluctuations affect the distribution of infectious diseases since this type of epidemic spreads randomly.…”

Section: Model Formulationmentioning

confidence: 99%

The Impact of Random Noise on the Dynamics of COVID-19 Epidemic Model

Koufi

Bennar

et al. 2022

Journal of Applied Mathematics

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At the end of 2019, the world knew the propagation of a new pandemic named COVID-19. This disease harmed the exercises of humankind and changed our way of life. For modeling and studying infectious illness transmission, mathematical models are helpful tools. Thus, in this paper, taking into account the effect of the intensity of the noises, we define a threshold value Π s of the model, which determines the extinction and persistence of the COVID-19 pandemic. We give numerical simulations to illustrate the analytical results.

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Section: Model Formulationmentioning

confidence: 99%

The Impact of Random Noise on the Dynamics of COVID-19 Epidemic Model

Koufi

Bennar

et al. 2022

Journal of Applied Mathematics

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“…Most of the authors previous focused on stabilization of CGNNs modeled by ordinary differential equations. In the wide range of applications, COVID -19 [22], dengue fever [23], HCV infections epidemic model [24], Alzheimer's disease [25], chemical reaction [26], and image encryption [27] are depend on both space and time variables. These behaviors are modeled by partial differential equations (PDE).…”

Section: Introductionmentioning

confidence: 99%

Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Gokulakrishnan

Sriniva

2023

Preprint

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The stabilization problem for a class of stochastic reaction-diffusion delayed Cohen-Grossberg neural networks (SRDDCGNNs) with event-triggered controller is addressed in this paper. To address such a problem, Neumann boundary condition, distributed and boundary external disturbances are introduced. New sufficient criteria are derived by using the Lyapunov method, event-triggered mechanism, and the linear matrix inequality (LMI) approach to ensure the proposed controlled systems achieve practically exponential input-to-state stabilization. In light of these criteria, the impact of an event-triggered controller on practically exponential input-to-state stability (PEISS) is examined. Furthermore , the obtained results are successfully applied to stochastic reaction-diffusion delayed cellular neural networks (SRDDCNNs) and stochastic reaction-diffusion delayed Hopfield neural networks (SRD-DHNNs). At last, simulation results are given to illustrate the main results, and the SRDDHNNs are applied to the image encryption.

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“…Several control methods have been suggested and successfully implemented to various epidemic diseases. The primary goal of the theory of optimal control, particularly in epidemiological problems, is to present a preventive measure that restricts the spread of the disease and to portray the transmission mechanism of the infections [59][60][61]. This theory has filled many loopholes in different fields, notably those that depends on dynamic system(s), such as physics and business [62,63].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Bifurcation and Stochastic Optimal Control of a Triple-Delayed Ebola Virus Model with Vaccination and Quarantine Strategies

2022

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Despite its high mortality rate of approximately 90%, the Ebola virus disease (EVD) has not received enough attention in terms of in-depth research. This illness has been responsible for over 40 years of epidemics throughout Central Africa. However, during 2014–2015, the Ebola-driven epidemic in West Africa became, and remains, the deadliest to date. Thus, Ebola has been declared one of the major public health issues. This paper aims at exploring the effects of external fluctuations on the prevalence of the Ebola virus. We begin by proposing a sophisticated biological system that takes into account vaccination and quarantine strategies as well as the effect of time lags. Due to some external perturbations, we extend our model to the probabilistic formulation with white noises. The perturbed model takes the form of a system of stochastic differential equations. Based on some non-standard analytical techniques, we demonstrate two main approach properties: intensity and elimination of Ebola virus. To better understand the impact of applied strategies, we deal with the stochastic control optimization approach by using some advanced theories. All of this theoretical arsenal has been numerically confirmed by employing some real statistical data of Ebola virus. Finally, we mention that this work could be a rich basis for further investigations aimed at understanding the complexity of Ebola virus propagation at pathophysiological and mathematics levels.

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Dynamics and optimal control of a stochastic coronavirus (COVID-19) epidemic model with diffusion

Cited by 19 publications

References 42 publications

The Impact of Random Noise on the Dynamics of COVID-19 Epidemic Model

The Impact of Random Noise on the Dynamics of COVID-19 Epidemic Model

Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Long-Term Bifurcation and Stochastic Optimal Control of a Triple-Delayed Ebola Virus Model with Vaccination and Quarantine Strategies

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