scholarly journals Modeling Public Opinion Polarization in Group Behavior by Integrating SIRS-Based Information Diffusion Process

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20 ◽  
Author(s):  
Tinggui Chen ◽  
Jiawen Shi ◽  
Jianjun Yang ◽  
Guodong Cong ◽  
Gongfa Li
Keyword(s):  
Epidemic Model ◽  
Social Transformation ◽  
Information Diffusion ◽  
Small World ◽  
Group Behavior ◽  
Experimental Simulation ◽  
Group Polarization ◽  
Polarization Model ◽  
Relationship Strength ◽  
Sirs Epidemic Model

Currently, China is in the period of social transformation. Such transformation continuously results in high group polarization behaviors, which attracts many attentions. In order to explore the evolutionary mechanism and formation process of group polarization behavior, this paper proposes a group polarization model which is integrated into the Susceptible-Infected-Recovered-Susceptible (SIRS) epidemic model. In this paper, firstly, the SIRS epidemic model and the factors of relationship strength are introduced based on the J-A model (proposed by Jager and Amblard) to enhance the information transmission and interaction among individuals. In addition, the BA network (proposed by Barabasi and Albert) model is used as the agent adjacency model due to its closeness to the real social network structure. After that, the Monte Carlo method is applied to conduct experimental simulation. Subsequently, this paper analyzes the simulation results in threefold: (1) comparison of polarization processes with and without integration of the SIRS epidemic model; (2) adjusting the immune recovery parameter γ and the relationship strength z to explore the role of these two parameters in the polarization process; and (3) comparing the polarization effects of different network structures. Through the experiments, we find that BA network is more polarized than small-world network in the same scale. Finally, corresponding measures are proposed to prevent and mitigate the occurrence of group polarization.

scholarly journals A Node-Based SIRS Epidemic Model with Infective Media on Complex Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Leyi Zheng ◽  
Longkun Tang
Keyword(s):  
Epidemic Model ◽  
Small World ◽  
Spreading Rate ◽  
Node Degree ◽  
Strong Positive Correlation ◽  
Globally Asymptotically Stable ◽  
Sirs Epidemic Model ◽  
Theoretical Investigations ◽  
Fully Connected ◽  
The Impact

We focus on the node-based epidemic modeling for networks, introduce the propagation medium, and propose a node-based Susceptible-Infected-Recovered-Susceptible (SIRS) epidemic model with infective media. Theoretical investigations show that the endemic equilibrium is globally asymptotically stable. Numerical examples of three typical network structures also verify the theoretical results. Furthermore, comparison between network node degree and its infected percents implies that there is a strong positive correlation between both; namely, the node with bigger degree is infected with more percents. Finally, we discuss the impact of the epidemic spreading rate of media as well as the effective recovered rate on the network average infected state. Theoretical and numerical results show that (1) network average infected percents go up (down) with the increase of the infected rate of media (the effective recovered rate); (2) the infected rate of media has almost no influence on network average infected percents for the fully connected network and NW small-world network; (3) network average infected percents decrease exponentially with the increase of the effective recovered rate, implying that the percents can be controlled at low level by an appropriate large effective recovered rate.

Dynamics of a stochastic susceptible-infective-recovered (SIRS) epidemic model with vaccination and nonlinear incidence under regime switching and Lévy jumps

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Junna Hu ◽  
Buyu Wen ◽  
Ting Zeng ◽  
Zhidong Teng
Keyword(s):  
Epidemic Model ◽  
Regime Switching ◽  
Markov Switching ◽  
Threshold Value ◽  
Open Problems ◽  
Nonlinear Incidence ◽  
Sirs Epidemic Model ◽  
Stochastic Epidemic ◽  
Lévy Jumps ◽  
White Noises

Abstract In this paper, a stochastic susceptible-infective-recovered (SIRS) epidemic model with vaccination, nonlinear incidence and white noises under regime switching and Lévy jumps is investigated. A new threshold value is determined. Some basic assumptions with regard to nonlinear incidence, white noises, Markov switching and Lévy jumps are introduced. The threshold conditions to guarantee the extinction and permanence in the mean of the disease with probability one and the existence of unique ergodic stationary distribution for the model are established. Some new techniques to deal with the Markov switching, Lévy jumps, nonlinear incidence and vaccination for the stochastic epidemic models are proposed. Lastly, the numerical simulations not only illustrate the main results given in this paper, but also suggest some interesting open problems.

Simulation and Relationship Strength: Characteristics of Knowledge Flows Among Subjects in a Regional Innovation System

2021 ◽  
pp. 097172182110204
Author(s):  
Yi Su ◽  
Xuesong Jiang ◽  
Zhouzhou Lin
Keyword(s):  
Innovation System ◽  
Small World ◽  
Coupled System ◽  
Mean Value ◽  
Knowledge Flows ◽  
Knowledge Flow ◽  
Regional Innovation System ◽  
Regional Innovation ◽  
Relationship Strength ◽  
Loosely Coupled

A small-world simulation model of a regional innovation system combining the strength of the intersubject relationship of the regional innovation system with the loosely coupled system is constructed. We use a simulation to observe knowledge flow within the regional innovation system under relationships of varying strength. The results show that when the relationship between the subjects of the regional innovation system reaches a certain strength, the system will exhibit high module independence and high network integrity, forming a loosely coupled system. The knowledge flow in the system exhibits the emergence of a fast flow rate, a high mean value and little variance. When relationship strength is at other levels, the emergence of knowledge cannot be identified.

scholarly journals Comparison of deterministic and stochastic SIRS epidemic model with saturating incidence and immigration

2014 ◽  
Vol 4 (2) ◽  
pp. 101-116 ◽  
Author(s):  
Aadil Lahrouz ◽  
Lahcen Omari ◽  
Adel Settati ◽  
Aziza Belmaâti
Keyword(s):  
Epidemic Model ◽  
Sirs Epidemic Model ◽  
Stochastic Sirs Epidemic Model

scholarly journals Dynamical Behavior of a Stochastic SIRS Epidemic Model

2015 ◽  
Vol 10 (2) ◽  
pp. 56-73 ◽  
Author(s):  
N. T. Hieu ◽  
N. H. Du ◽  
P. Auger ◽  
N. H. Dang
Keyword(s):  
Epidemic Model ◽  
Dynamical Behavior ◽  
Sirs Epidemic Model ◽  
Stochastic Sirs Epidemic Model

scholarly journals When will the Covid-19 epidemic fade out?

2020 ◽  
Author(s):  
Ilaria Renna
Keyword(s):  
Infectious Diseases ◽  
Discrete Time ◽  
Epidemic Model ◽  
Small World ◽  
Small World Network ◽  
Sirs Model ◽  
Gaussian Fit

AbstractA discrete-time deterministic epidemic model is proposed with the aim of reproducing the behaviour observed in the incidence of real infectious diseases. For this purpose, we analyse a SIRS model under the framework of a small world network formulation. Using this model, we make predictions about the peak of the Covid-19 epidemic in Italy. A Gaussian fit is also performed, to make a similar prediction.

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