scholarly journals Epidemic Spreading with Heterogeneous Awareness on Human Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Yanling Lu ◽  
Guoping Jiang ◽  
Zhengxin Wang
Keyword(s):  
Theoretical Analysis ◽  
Epidemic Model ◽  
Arrival Time ◽  
Behavioral Responses ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Epidemic Dynamics ◽  
Awareness Information ◽  
The Impact ◽  
Human Networks

The spontaneous awareness behavioral responses of individuals have a significant impact on epidemic spreading. In this paper, a modified Susceptible-Alert-Infected-Susceptible (SAIS) epidemic model with heterogeneous awareness is presented to study epidemic spreading in human networks and the impact of heterogeneous awareness on epidemic dynamics. In this model, when susceptible individuals receive awareness information about the presence of epidemic from their infected neighbor nodes, they will become alert individuals with heterogeneous awareness rate. Theoretical analysis and numerical simulations show that heterogeneous awareness can enhance the epidemic threshold with certain conditions and reduce the scale of virus outbreaks compared with no awareness. What is more, for the same awareness parameter, it also shows that heterogeneous awareness can slow effectively the spreading size and does not delay the arrival time of epidemic spreading peak compared with homogeneous awareness.

The analysis of epidemic spreading on clique-overlapping growth network

2020 ◽  
Vol 34 (26) ◽  
pp. 2050235
Author(s):  
Zhenzhou Lin
Keyword(s):  
Theoretical Analysis ◽  
Mean Field Theory ◽  
Mean Field ◽  
Epidemic Outbreak ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Epidemic Dynamics ◽  
Immunization Strategies ◽  
The Mean ◽  
Real Complex

In this paper, we propose a new clique-overlapping growth network and study the epidemic spreading on it. We verify by simulation and theoretical analysis that the degree distribution follows a power-law form. Then, we have simulated the epidemic dynamics in this clique-overlapping growth network. Based on the mean-field theory, we have obtained the theoretical epidemic threshold. We find that the epidemic threshold is related to the evolution mechanism of the network model. The theoretical analysis is well consistent with the simulation results. The results in this model can help people understand the epidemic spreading of various processes, such as infectious diseases, computer viruses, gossips, and so on in real complex networks. Moreover, the appropriate immunization strategies can also be designed based on our results, to hold back the trend of epidemic outbreak.

THEORETICAL INVESTIGATION OF THE IMPACT ON EPIDEMIC THRESHOLD OF TRAVEL BETWEEN COMMUNITIES OF RESIDENT POPULATIONS

2013 ◽  
Vol 21 (02) ◽  
pp. 1350010 ◽  
Author(s):  
KLOT PATANARAPEELERT ◽  
D. GARCIA LOPEZ ◽  
I-MING TANG ◽  
MARC A. DUBOIS
Keyword(s):  
Basic Reproduction Number ◽  
Epidemic Model ◽  
Initial Phase ◽  
Reproduction Number ◽  
Travel Patterns ◽  
Epidemic Threshold ◽  
Contact Patterns ◽  
Resident Populations ◽  
The Impact ◽  
The Basic Reproduction Number

During the initial phase of an epidemic, individual displacements between different regions modify the contact patterns. Understanding mobility processes and their consequences is necessary to predict the subsequent spread of the disease in order to optimize control policies. The basic reproduction number is commonly used to determine the threshold between extinction and expansion of the disease. Once it is derived for an epidemic model that includes the travel of population between distinct localities, the dependence of the diseases dynamics upon travel rates becomes explicit. In this study, we examine the effects of travel on the epidemic threshold for a model of two communities. The travel rates are treated as varying subject to two scenarios. We show theoretically that if the transmission potentials within communities are moderate, the epidemic threshold can be modified by travel. The conditions for the presence of the threshold induced by travel is determined and the critical values of travel at which the basic reproduction number is equal to one are derived. We show further that these results can also be applied to a model of three communities under specific travel patterns and that the derived basic reproduction number has a form similar to that of the two communities problem.

DISCRETE-TIME EPIDEMIC DYNAMICS WITH AWARENESS IN RANDOM NETWORKS

2013 ◽  
Vol 06 (02) ◽  
pp. 1350007 ◽  
Author(s):  
YILUN SHANG
Keyword(s):  
Discrete Time ◽  
Behavioral Responses ◽  
Random Networks ◽  
Global Awareness ◽  
Risk Of Infection ◽  
Epidemic Threshold ◽  
Epidemic Dynamics ◽  
Local Awareness ◽  
The Stability ◽  
Sis Epidemic

Human behavioral responses fundamentally influence the spread of infectious disease. In this paper, we study a discrete-time SIS epidemic process in random networks. Three forms of individual awareness, namely, local awareness, global awareness and contact awareness, are considered. The effect of awareness is to reduce the risk of infection. Based on the stability theory of matrix difference equation, we derive analytically the epidemic threshold. It is found that both local and contact awareness can raise the epidemic threshold, while the global awareness only decreases the epidemic prevalence. Our results are in line with a recent result using differential equation-based methods.

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.

Joint effect of individual’s memory and attractiveness in temporal network on spreading dynamics

2019 ◽  
Vol 30 (01) ◽  
pp. 1950011 ◽  
Author(s):  
Mei Yang ◽  
Bing Wang ◽  
Yuexing Han
Keyword(s):  
Network Model ◽  
Joint Effect ◽  
Network Evolution ◽  
Disease Spread ◽  
Temporal Network ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Modeling Framework ◽  
Positive Correlations ◽  
The Impact

Most real networks have been found to evolve dynamically. In addition, heterogeneity of interaction strengths and individual’s memory have been proved to be important to affect network evolution in the area of complex networks. Based on a recent extension of the activity-driven modeling framework, i.e. activity-driven model with attractiveness, where individuals are characterized by their activities and attractiveness, we propose a new temporal network model by considering individuals’ memory. Similar to the previous network model, we also investigate heterogeneous distributions and different correlations between the two variables (activity and attractiveness). By numerical simulations, we illustrate the impact of memory on epidemic spreading processes unfolding on the proposed model. We find that, when individuals’ attractiveness is fixed, memory does not cause dramatic change on the epidemic threshold in the SIR process, but it inhibits the epidemic spreading by reducing the infected ratio. While for the SIS process, in the case of uncorrelated and positive correlations between the two variables, memory facilitates the epidemic outbreaks by shrinking the epidemic threshold. When the two variables are negatively correlated, memory does not take obvious impact on the epidemic threshold. Besides, for the two processes unfolding on the network with and without memory, the propagation threshold decreases with the enhancement of the positive correlation, however, it almost does not get much influence from the negative correlation strength. It indicates that for the individuals with same activity, more attractive individuals will promote the disease spread. The findings deepen our understanding of the role of individuals’ memory and their attractiveness in the epidemic spreading process.

Spreading Dynamics Analysis of a Novel SEAIRS Epidemic Model with Protection and Hospital Quarantine under Government Pre-warning

2021 ◽  
Author(s):  
Bingchuan Xue ◽  
Tao Li ◽  
Xinming Cheng ◽  
Yumiao Li ◽  
Yuanyuan Wu ◽  
...  
Keyword(s):  
Epidemic Model ◽  
Mean Field Theory ◽  
Mean Field ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Epidemic Spreading ◽  
Scale Free ◽  
Spreading Dynamics ◽  
The Mean ◽  
The Impact

Abstract To study the impact of protection and hospital quarantine measure, government pre-warning mechanism and heterogeneity of underlying networks on epidemic spreading, a novel SEAIRS epidemic model is proposed on scale-free networks. The spreading dynamics of the model is studied by means of the mean-field theory. Two equilibriums and the basic reproductive number R0 of the model is analyzed in detail. The global asymptotic stability of the disease-free equilibrium, the permanence of the epidemic spreading and the global attractivity of the endemic equilibrium are proved. Sensitivity analysis shows that the basic reproductive number R0 is dependent on the coverage rate of home quarantine (ωQ,ηA ,ηS ), hospitalization rate η1 and government pre-warning intensity δ . Finally, the theoretical analysis results are confirmed by means of numerical simulations.

scholarly journals The Impact of the Global and Local Awareness Diffusion on Epidemic Transmission Considering the Heterogeneity of Individual Influences

2022 ◽  
Author(s):  
Haidong Xu ◽  
Ye Zhao ◽  
Dun Han
Keyword(s):  
Mean Field ◽  
Global Awareness ◽  
Protective Behavior ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Spreading Model ◽  
Information Layer ◽  
Local Awareness ◽  
Global And Local ◽  
The Impact

Abstract In this paper, we propose a coupled awareness - epidemic spreading model considering the heterogeneity of individual influences, which aims to explore the interaction between awareness diffusion and epidemic transmission. The considered heterogeneity of individual influences are threefold: the heterogeneity of individual influences in the information layer, the heterogeneity of individual influences in the epidemic layer and the heterogeneity of individual behavioral responses to epidemics. In addition, the individuals' receptive preference for information and the impacts of individuals' perceived local awareness ratio and individuals' perceived epidemic severity on self-protective behavior are included. The epidemic threshold is theoretically established according to the microscopic Markov chain approach and mean-field approach. Results indicate that the critical local and global awareness ratios have two-stage effects on the epidemic threshold. Besides, either the heterogeneity of individual influences in the information layer or the strength of individuals' responses to epidemics can influence the epidemic threshold with a nonlinear way. However, the heterogeneity of individual influences in the epidemic layer has few effect on the epidemic threshold, but can affects the magnitude of the final infected density.

scholarly journals Self-Awareness-Based Resource Allocation Strategy for Containment of Epidemic Spreading

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaolong Chen ◽  
Quanhui Liu ◽  
Ruijie Wang ◽  
Qing Li ◽  
Wei Wang
Keyword(s):  
Resource Allocation ◽  
Network Structure ◽  
Coupled Model ◽  
Tuning Parameter ◽  
Self Awareness ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Optimal Point ◽  
Allocation Model ◽  
The Impact

Resource support between individuals is of particular importance in controlling or mitigating epidemic spreading, especially during pandemics. However, there remains the question of how we can protect ourselves from being infected while helping others by donating resources in fighting against the epidemic. To answer the question, we propose a novel resource allocation model by considering the awareness of self-protection of individuals. In the model, a tuning parameter is introduced to quantify the reaction strength of individuals when they are aware of the disease. And then, a coupled model of resource allocation and disease spreading is proposed to study the impact of self-awareness on resource allocation and its impact on the dynamics of epidemic spreading. Through theoretical analysis and extensive Monte Carlo simulations, we find that in the stationary state, the system converges to two states: the whole healthy or the completely infected, which indicates an abrupt increase in the prevalence when there is a shortage of resources. More importantly, we find that too cautious and too selfless for the people during the outbreak of an epidemic are both not suitable for disease control. Through extensive simulations, we locate the optimal point, at which there is a maximum value of the epidemic threshold, and an outbreak can be delayed to the greatest extent. At last, we study further the effects of the network structure on the coupled dynamics. We find that the degree heterogeneity promotes the outbreak of disease, and the network structure does not alter the optimal phenomenon in behavior response. Based on the results of this study, a constructive suggestion is that in the face of a global pandemic, individuals or countries should strengthen mutual support and cooperation while doing their own prevention to suppress the epidemic optimally.

Finite size effects in epidemic spreading: the problem of overpopulated systems

Open Physics ◽  
2013 ◽  
Vol 11 (12) ◽  
Author(s):  
Wojciech Ganczarek
Keyword(s):  
Mixing Time ◽  
Finite Size ◽  
Network Size ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Time Step ◽  
Finite Size Effects ◽  
Sexually Transmitted ◽  
The Impact ◽  
Quasi Stationary Distribution

AbstractIn this paper we analyze the impact of network size on the dynamics of epidemic spreading. In particular, we investigate the pace of infection in overpopulated systems. In order to do that, we design a model for epidemic spreading on a finite complex network with a restriction to at most one contamination per time step, which can serve as a model for sexually transmitted diseases spreading in some student communes. Because of the highly discrete character of the process, the analysis cannot use the continuous approximation widely exploited for most models. Using a discrete approach, we investigate the epidemic threshold and the quasi-stationary distribution. The main results are two theorems about the mixing time for the process: it scales like the logarithm of the network size and it is proportional to the inverse of the distance from the epidemic threshold.

scholarly journals Optimal Control Strategy for Traffic Driven Epidemic Spreading Based on Community Structure

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Shao ◽  
Guo-Ping Jiang
Keyword(s):  
Community Structure ◽  
Control Strategy ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Optimal Control Strategy ◽  
Scale Free ◽  
Second Moments ◽  
Scale Free Networks ◽  
Novel Strategy ◽  
The Impact

It is shown that community structure has a great impact on traffic transportation and epidemic spreading. The density of infected nodes and the epidemic threshold have been proven to have significant relationship with the node betweenness in traffic driven epidemic spreading method. In this paper, considering the impact of community structure on traffic driven epidemic spreading, an effective and novel strategy to control epidemic spreading in scale-free networks is proposed. Theoretical analysis shows that the new control strategy will obviously increase the ratio between the first and the second moments of the node betweenness distribution in scale-free networks. It is also found that the more accurate the community is identified, the stronger community structure the network has and the more efficient the control strategy is. Simulations on both computer-generated and real-world networks have confirmed the theoretical results.

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