Vaccination controllers for SEIR epidemic models based on fractional order dynamics

Ullah, Nasim; Ibeas, Asier; Shafi, Muhammad; Ishfaq, Muhammad; Ali, Mumtaz

doi:10.1016/j.bspc.2017.05.013

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…Remark 1: Comparing with the SEIR epidemic model in [1][2][3][4][5][6][7][8][9][10][11][12][13][14], the epidemic model (6) is more general. In the model (6), many factors considered and added according to the latest discovery of life science and medicine, which means that the model (6) proposed in this paper can more truly reflect the spread of infectious diseases in the real environment.…”

Section: Dynamics Of Recovered Populationmentioning

confidence: 99%

“…Hence, the classical SEIR epidemic model are modified by adding addition terms, which denote addition factors [14]. In [3][4][5][6], the newborn was considered in the dynamics of susceptible subpopulation S(t). In [8][9][10], the susceptible individuals were assumed to be increased at any time t by the recruitment rate.…”

Section: Introductionmentioning

confidence: 99%

“…From the latest research in life science, some recovered individuals may become susceptible once again, which is called losing of immune or immune failure. In [6][7][8][10][11][12], immune failure was considered as a factor affecting the population of S(t) and R(t). Recently, the nature death rate were also considered in the SEIR epidemic model.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the nature death rate were also considered in the SEIR epidemic model. The latest research and observation show that the dynamics of epidemics is influenced by too many factors, In the above works [3][4][5][6][7][8][9][10][11][12][13][14], however, only some of the factors are concerned. Obviously, the considered models are applicable in some specific situations, which limit their application scopes.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, by using the advanced control technique [18][19][20][21][24][25][26][27][28][29], many researchers have proposed a lots of control strategies, foe instance, treatment strategies [3], vaccination strategies [4,6], feedback-control-based strategies [11], and so on. As stated previously, only special models (incomplete or imprecise models) were considered in the above works [3][4][5][6]11]. For general epidemic models, how to design effective and appropriate control actions should be further studied, which still motivates us for this work.…”

Section: Introductionmentioning

confidence: 99%

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Dynamics Analysis and Vaccination-Based Sliding Mode Control of a More Generalized SEIR Epidemic Model

Jiao

Shen

2020

IEEE Access

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In this paper, based on the latest discovery of epidemiology, a more general SEIR epidemic model is firstly proposed and its main properties such as positivity and boundedness have been analyzed and proven. Meanwhile, a method is proposed to solve basic reproduction number. In addition, a vaccinationbased sliding mode control strategy is designed to guarantee that the proportion of infected subpopulation in the total population converge to the desired signal. Finally, by using computer simulation, the positivity and boundedness of the epidemic model and some control strategies are verified and evaluated, respectively.

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Section: Dynamics Of Recovered Populationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamics Analysis and Vaccination-Based Sliding Mode Control of a More Generalized SEIR Epidemic Model

Jiao

Shen

2020

IEEE Access

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Sliding mode control of the susceptible‐exposed‐asymptomatic‐infected‐recovered model with vaccination

Chen,

Xiang,

Yang

2024

Math Methods in App Sciences

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This paper studies a nonlinear epidemiological model called susceptible, exposed, asymptomatic infected, infected, and recovered (SEAIR) with a control input (vaccination), which helps to design a sliding mode vaccination strategy by tracking the prevalence of infectious diseases. First, the effective reproduction number is adjusted to the expected value to control infectious disease outbreaks, even with model parameter changes and systematic perturbations. Meanwhile, based on the sliding mode control theory, two control strategies, namely, first‐order sliding mode control and super‐twisting control, are proposed to control the spread of COVID‐19. Besides, an integral sliding surface associated with the effective reproduction number is constructed to eliminate the chattering problem of the system. Moreover, the sliding mode controller is robust to uncertainties and external perturbations. The stability and finite‐time convergence of the closed‐loop control system are verified by Lyapunov's second method. Also, the effect of the two control strategies on the system performance is evaluated by numerical simulations. Furthermore, this paper presents a practical case simulation based on data from Wuhan. The results indicate that universal vaccination is crucial in the early stage of the COVID‐19 pandemic. As the epidemic progresses, vaccination is still an essential approach for epidemic prevention and control.

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A wavelet based numerical scheme for fractional orderSEIRepidemic of measles by using Genocchi polynomials

Kumar

Osman

et al. 2020

Numerical Methods Partial

163

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Epidemiology is the glorious discipline underlying medical research, public health practice, and health care evaluation. Nowadays, research on disease models with anonymous parameters is a popular issue for researchers working in epidemiology. Due to popularity of this field, a new numerical method for the solution of the fractional SEIR epidemic of measles is introduced where fractional derivative is taken in Caputo sense. We have discussed about the framework of Genocchi wavelets for numerical simulations of above disease model. Furthermore, the operational matrix merged with the collocation method is used in order to convert fractional‐order problem into algebraic equations. The Adams‐Bashforth‐Moulton (ABM) numerical scheme is used to solve above disease model with various parameters. For we have compared the solutions with Adams‐Bashforth‐Moulton predictor corrector scheme for the accuracy and applicability of the Genocchi wavelets method (GWM). The behaviors of susceptible, exposed, infected, and recovered individuals are presented graphically at the value of various fractional order. The error and convergence analysis of the Genocchi wavelets has been discussed for the applicability of the present methods. Further, various numerical simulations have been carried out to justify our achieved finding.

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Vaccination controllers for SEIR epidemic models based on fractional order dynamics

Cited by 17 publications

References 28 publications

Dynamics Analysis and Vaccination-Based Sliding Mode Control of a More Generalized SEIR Epidemic Model

Dynamics Analysis and Vaccination-Based Sliding Mode Control of a More Generalized SEIR Epidemic Model

Sliding mode control of the susceptible‐exposed‐asymptomatic‐infected‐recovered model with vaccination

A wavelet based numerical scheme for fractional orderSEIRepidemic of measles by using Genocchi polynomials

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