Stability analysis and control strategies for a new SIS epidemic model in heterogeneous networks

Xie, Yingkang; Wang, Zhen; Lu, Junwei; Li, Yuxia

doi:10.1016/j.amc.2020.125381

Cited by 19 publications

(11 citation statements)

References 27 publications

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“…Hence it is wise to consider a wide class of interaction functionals, which provides a wide choice of applications of the obtained results in predicting the evolution of the species. For more reading about some recent methods of modeling ecological interactions, we refer the readers to [52,53]. The applicability of model ( 1) is an additional motivation for us to present this paper.…”

Section: Introductionmentioning

confidence: 99%

Dynamical behavior of two predators–one prey model with generalized functional response and time-fractional derivative

Djilali

Ghanbari

2021

Adv Differ Equ

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The behavior of any complex dynamic system is a natural result of the interaction between the components of that system. Important examples of these systems are biological models that describe the characteristics of complex interactions between certain organisms in a biological environment. The study of these systems requires the use of precise and advanced computational methods in mathematics. In this paper, we discuss a prey–predator interaction model that includes two competitive predators and one prey with a generalized interaction functional. The primary presumption in the model construction is the competition between two predators on the only prey, which gives a strong implication of the real-world situation. We successfully establish the existence and stability of the equilibria. Further, we investigate the impact of the memory measured by fractional time derivative on the temporal behavior. We test the obtained mathematical results numerically by a proper numerical scheme built using the Caputo fractional-derivative operator and the trapezoidal product-integration rule.

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Section: Introductionmentioning

confidence: 99%

Dynamical behavior of two predators–one prey model with generalized functional response and time-fractional derivative

Djilali

Ghanbari

2021

Adv Differ Equ

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“…People who have not gotten the disease are labeled "susceptibles"; while those who have been infected by a certain disease are called "infectives". This kind of compartment model is denoted by "an SIS epidemic model" [2][3][4][5][6][7], which has been extensively used to address the dynamics of those diseases, describing an individual infected by a disease as having no immunity, thus becoming a susceptible again.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, the social interactions of individuals generate a certain pattern based on social preferences, which contributes to transmission heterogeneity. Indeed, such factors may play a decisive role in the disease transmission and they also may help health policymakers to take more effective control measures for curbing the disease spread [7,8]. Epidemic models on complex networks incorporate such contact heterogeneity and take account for how the structures of the networks affect the disease prevalence.…”

Section: Introductionmentioning

confidence: 99%

A Bistable Phenomena Induced by a Mean-Field SIS Epidemic Model on Complex Networks: A Geometric Approach

Wang

Yang

2021

Front. Phys.

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In this paper, we propose a degree-based mean-field SIS epidemic model with a saturated function on complex networks. First, we adopt an edge-compartmental approach to lower the dimensions of such a proposed system. Then we give the existence of the feasible equilibria and completely study their stability by a geometric approach. We show that the proposed system exhibits a backward bifurcation, whose stabilities are determined by signs of the tangent slopes of the epidemic curve at the associated equilibria. Our results suggest that increasing the management and the allocation of medical resources effectively mitigate the lag effect of the treatment and then reduce the risk of an outbreak. Moreover, we show that decreasing the average of a network sufficiently eradicates the disease in a region or a country.

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“…In addition, we see that some new research works on the population and epidemic dynamical models are published. For example, in [39], a new SIS epidemic model considering the infection rates of multiple edges interfere with each other on complex networks was proposed. In [40], a class of fractional-order prey-predator models with Holling III type functional response and discontinuous harvest is investigated.…”

Section: Introduction Echinococcosis Is Mainly a Zoonotic Parasitic mentioning

confidence: 99%

“…In this paper, motivated by the results given in [2,35,34,38,39,40], we use the compartment modelling method (see [12,25]) to establish a discrete-time dynamical model for the transmission of echinococcosis. The technical contribution in this paper is that we use Euler backward difference method to transform the partial differential equations into discrete equations, thus simplifying the solution process.…”

Section: Introduction Echinococcosis Is Mainly a Zoonotic Parasitic mentioning

confidence: 99%

The threshold dynamics of a discrete-time echinococcosis transmission model

Cui-cui¹,

Zhou²,

Teng³

et al. 2020

DCDS-B

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Stability analysis and control strategies for a new SIS epidemic model in heterogeneous networks

Cited by 19 publications

References 27 publications

Dynamical behavior of two predators–one prey model with generalized functional response and time-fractional derivative

Dynamical behavior of two predators–one prey model with generalized functional response and time-fractional derivative

A Bistable Phenomena Induced by a Mean-Field SIS Epidemic Model on Complex Networks: A Geometric Approach

The threshold dynamics of a discrete-time echinococcosis transmission model

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