An SIS Epidemic Model with Feedback Mechanism in Scale-Free Networks

2011 ◽  
Vol 204-210 ◽  
pp. 354-358 ◽  
Author(s):  
Guang Wu Gong ◽  
Da Min Zhang
Keyword(s):  
Epidemic Model ◽  
Feedback Mechanism ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Spreading Process ◽  
Scale Free ◽  
Sis Epidemic Model ◽  
Scale Free Networks ◽  
Controlling Measures ◽  
Sis Epidemic

A new susceptible-infected-susceptible model with feedback mechanism is proposed. The dynamic behavior of the epidemic model with feedback mechanism in scale-free networks is researched by theoretical analysis and computer simulation. The results show that feedback mechanism can reduce the stable infective ratio of system; however, it can not influence the epidemic threshold of system. The results can help us to understand rightly epidemic spreading process in reality networks and guide people to design effective epidemic preventive and controlling measures when epidemic outbreaks.

scholarly journals An SIS Epidemic Model with Infective Medium and Feedback Mechanism on Scale-Free Networks

OALib ◽  
2017 ◽  
Vol 04 (05) ◽  
pp. 1-9
Author(s):  
Xiongding Liu ◽  
Tao Li ◽  
Yuanmei Wang ◽  
Chen Wan ◽  
Jing Dong
Keyword(s):  
Epidemic Model ◽  
Feedback Mechanism ◽  
Scale Free ◽  
Sis Epidemic Model ◽  
Scale Free Networks ◽  
Sis Epidemic

scholarly journals The bifurcation analysis and optimal feedback mechanism for an SIS epidemic model on networks

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Lijuan Chen ◽  
Shouying Huang ◽  
Fengde Chen ◽  
Mingjian Fu
Keyword(s):  
Optimal Control ◽  
Bifurcation Analysis ◽  
Epidemic Model ◽  
Feedback Mechanism ◽  
Optimal Control Strategy ◽  
Optimal Feedback ◽  
Sis Epidemic Model ◽  
Vital Effect ◽  
Bifurcation Behavior ◽  
Sis Epidemic

AbstractIt is well known that the feedback mechanism or the individual’s intuitive response to the epidemic can have a vital effect on the disease’s spreading. In this paper, we investigate the bifurcation behavior and the optimal feedback mechanism for an SIS epidemic model on heterogeneous networks. Firstly, we present the bifurcation analysis when the basic reproduction number is equal to unity. The direction of bifurcation is also determined. Secondly, different from the constant coefficient in the existing literature, we incorporate a time-varying feedback mechanism coefficient. This is more reasonable since the initiative response of people is constantly changing during different process of disease prevalence. We analyze the optimal feedback mechanism for the SIS epidemic network model by applying the optimal control theory. The existence and uniqueness of the optimal control strategy are obtained. Finally, a numerical example is presented to verify the efficiency of the obtained results. How the topology of the network affects the optimal feedback mechanism is also discussed.

Research on SIQRS Spreading Dynamic Model with Feedback Mechanism on Scale-Free Networks

2014 ◽  
Vol 989-994 ◽  
pp. 4524-4527
Author(s):  
Tao Li ◽  
Yuan Mei Wang ◽  
You Ping Yang
Keyword(s):  
Dynamic Model ◽  
Mean Field Theory ◽  
Mean Field ◽  
Feedback Mechanism ◽  
Epidemic Spreading ◽  
Scale Free ◽  
Spreading Dynamics ◽  
Scale Free Networks ◽  
The Mean ◽  
The Relationship

A modified spreading dynamic model with feedback-mechanism based on scale-free networks is presented in this study. Using the mean field theory, the spreading dynamics of the model is analyzed. The spreading threshold and equilibriums are derived. The relationship between the spreading threshold, the epidemic steady-state and the feedback-mechanism is analyzed in detail. Theoretical results indicate the feedback-mechanism can increase the spreading threshold, resulting in effectively controlling the epidemic spreading.

scholarly journals Traffic-driven epidemic spreading on scale-free networks with tunable degree distribution

2016 ◽  
Vol 27 (11) ◽  
pp. 1650125 ◽  
Author(s):  
Han-Xin Yang ◽  
Bing-Hong Wang
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Network Structures ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Scale Free ◽  
Scale Free Networks ◽  
Law Degree

We study the traffic-driven epidemic spreading on scale-free networks with tunable degree distribution. The heterogeneity of networks is controlled by the exponent [Formula: see text] of power-law degree distribution. It is found that the epidemic threshold is minimized at about [Formula: see text]. Moreover, we find that nodes with larger algorithmic betweenness are more likely to be infected. We expect our work to provide new insights in to the effect of network structures on traffic-driven epidemic spreading.

Epidemic spreading on networks based on stress response

2017 ◽  
Vol 31 (16) ◽  
pp. 1750131 ◽  
Author(s):  
Fuzhong Nian ◽  
Shuanglong Yao
Keyword(s):  
Stress Response ◽  
Infectious Diseases ◽  
Stress Responses ◽  
Epidemic Model ◽  
Small World ◽  
Small World Networks ◽  
Epidemic Spreading ◽  
Spreading Resistance ◽  
Scale Free ◽  
Scale Free Networks

Based on the stress responses of individuals, the susceptible-infected-susceptible epidemic model was improved on the small-world networks and BA scale-free networks and the simulations were implemented and analyzed. Results indicate that the behaviors of individual’s stress responses could induce the epidemic spreading resistance and adaptation at the network level. This phenomenon showed that networks were learning how to adapt to the disease and the evolution process could improve their immunization to future infectious diseases and would effectively prevent the spreading of infectious diseases.

scholarly journals An SIS epidemic model with time delay and stochastic perturbation on heterogeneous networks

2021 ◽  
Vol 18 (5) ◽  
pp. 6790-6805
Author(s):  
Meici Sun ◽  
◽  
Qiming Liu
Keyword(s):  
Time Delay ◽  
Epidemic Model ◽  
Deterministic Model ◽  
Reproduction Number ◽  
Sufficient Conditions ◽  
Stochastic Perturbation ◽  
Scale Free ◽  
Sis Epidemic Model ◽  
The Mean ◽  
Sis Epidemic

<abstract><p>An SIS epidemic model with time delay and stochastic perturbation on scale-free networks is established in this paper. And we derive sufficient conditions guaranteeing extinction and persistence of epidemics, respectively, which are related to the basic reproduction number $ R_0 $ of the corresponding deterministic model. When $ R_0 &lt; 1 $, almost surely exponential extinction and $ p $-th moment exponential extinction of epidemics are proved by Razumikhin-Mao Theorem. Whereas, when $ R_0 &gt; 1 $, the system is persistent in the mean under sufficiently weak noise intensities, which indicates that the disease will prevail. Finally, the main results are demonstrated by numerical simulations.</p></abstract>

An epidemic spreading model on adaptive scale-free networks with feedback mechanism

2016 ◽  
Vol 450 ◽  
pp. 649-656 ◽  
Author(s):  
Tao Li ◽  
Xiongding Liu ◽  
Jie Wu ◽  
Chen Wan ◽  
Zhi-Hong Guan ◽  
...  
Keyword(s):  
Feedback Mechanism ◽  
Epidemic Spreading ◽  
Scale Free ◽  
Spreading Model ◽  
Scale Free Networks

Epidemic Spreading with Feedback Mechanism and Time Delay under Migration in Scale-Free Networks

2013 ◽  
Vol 378 ◽  
pp. 655-661
Author(s):  
Tao Li ◽  
Yuan Mei Wang
Keyword(s):  
Time Delay ◽  
Mean Field Theory ◽  
Mean Field ◽  
Feedback Mechanism ◽  
Critical Threshold ◽  
Epidemic Spreading ◽  
Scale Free ◽  
Epidemic Dynamics ◽  
Scale Free Networks ◽  
The Mean

Taking into account the heterogeneity of the underlying networks, an epidemic model with feedback-mechanism, time delay and migrations of individuals on scale-free networks is presented. First, the epidemic dynamics is analyzed via the mean field theory. The spreading critical threshold and equilibriums are derived. The existence of endemic equilibrium is determined by the spreading threshold. Then, the influences of feedback-mechanism, time delay, migrations of individuals and the heterogeneity of the scale-free networks on the spreading threshold and the epidemic steady-state are studied in detail. Numerical simulations are presented to illustrate the results with the theoretical analysis.

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