The global dynamics for a stochastic SIS epidemic model with isolation

Chen, Yiliang; Wen, Buyu; Teng, Zhidong

doi:10.1016/j.physa.2017.11.085

Cited by 34 publications

(19 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the disease transmission coefficient in epidemic models was modified for meteorological factors because survivals and infectivity of many viruses and bacteria are better in damp conditions with little ultraviolet light [21]. Thus, many researchers have introduced parameter perturbation into epidemic models to show the effect of environmental white noise [14,24,25,31,30,4,5,6,13,29,34,16]. For example, Ji and Jiang [14] explored the long time behavior of a multigroup SIS model with stochastic perturbations.…”

Section: Qun Liu and Daqing Jiangmentioning

confidence: 99%

Dynamics of a multigroup SIRS epidemic model with random perturbations and varying total population size

Li¹,

Jiang²

2020

Communications on Pure &Amp; Applied Analysis

View full text Add to dashboard Cite

In this paper, we analyze a multigroup SIRS epidemic model with random perturbations and varying total population size. By utilizing the stochastic Lyapunov function method, we establish sufficient conditions for the existence of a stationary distribution of the positive solutions to the model. Since our model is multidimensional, it is extremely difficult to construct an appropriate stochastic Lyapunov function to prove the existence of the stationary distribution, which implies stochastic weak stability. Then we establish sufficient conditions for extinction of the diseases. These conditions are related to the basic reproduction number in its corresponding deterministic system.

show abstract

Section: Qun Liu and Daqing Jiangmentioning

confidence: 99%

Dynamics of a multigroup SIRS epidemic model with random perturbations and varying total population size

Li¹,

Jiang²

2020

Communications on Pure &Amp; Applied Analysis

View full text Add to dashboard Cite

show abstract

“…Incorporation of different intervention strategies into the model can be executed in different ways. For instance, to model the behavioural change such as awareness [10,11], isolation [12,13] or to study the impact of vaccination [14], an extra compartment is usually introduced in the model. On the other hand, in the context of vector borne disease modelling, incorporation of self protection such as use of bed net, application of vector repellent on skin, etc.…”

Section: Introductionmentioning

confidence: 99%

Disease control through removal of population using Z-control approach

Senapati

Panday

Samanta

et al. 2020

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

a b s t r a c tPresent study considers the situation where the removal of population is adopted as a prevention measure for isolating the susceptible population from an infected region to reduce the disease prevalence. To investigate the scenario, a dynamic error based method, Z-type control is applied in an SI type disease model with the aim of achieving a predetermined disease prevalence. The controlled system is designed by introducing a new compartment (the population in an infection-free region) in the uncontrolled system to capture the removal of susceptible population from the infected region to an infection free region. By performing numerical simulations, our study shows that using Z-control mechanism, the removal of susceptible to an infection free region can effectively achieve a predetermined disease prevalence. The removal rates required for achieving a specific reduction in infected population for different levels of disease endemicity are quantified. Furthermore, the global sensitivity analysis (PRCC) is also performed to have a more clear insights on the correlations of the control parameter with the model parameters.

show abstract

“…Hence, a stochastic epidemic model has more practical significance. There are various possible methods for applying the modeling skills of stochastic differential equations to introduce environmental variability into epidemic models [6,12,13,14,15,16,17,18,19,20,21]. Particularly, Zhao and Jiang [18] investigated the dynamics of a stochastic SIRS epidemic model with saturated incidence.…”

Section: Introductionmentioning

confidence: 99%

“…The sufficient conditions on the extinction almost surely of the disease and the existence of stationary distribution of the model are established. Chen, Wen and Teng [21] discussed the global dynamics for a stochastic SIS epidemic model with isolation. They obtained the threshold of the system for the extinction and permanence in the mean of the disease.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of a stochastic sheep brucellosis model with immigration

2020

J. Nonlinear Funct. Anal.

View full text Add to dashboard Cite

Sheep brucellosis is one of the principal infective and contagious bacterial diseases among sheep populations in pastoral areas of China. In this paper, we establish a stochastic sheep brucellosis model by involving the effect of environmental white noise for the propagation process of brucellosis. We use the stochastic Lyapunov function theory to construct a suitable stochastic Lyapunov function for the existence of global positive solution. We also obtain the sufficient conditions for the disease extinction and the disease persistence.

show abstract

The global dynamics for a stochastic SIS epidemic model with isolation

Cited by 34 publications

References 45 publications

Dynamics of a multigroup SIRS epidemic model with random perturbations and varying total population size

Dynamics of a multigroup SIRS epidemic model with random perturbations and varying total population size

Disease control through removal of population using Z-control approach

Dynamics of a stochastic sheep brucellosis model with immigration

Contact Info

Product

Resources

About