Information Diffusion And Epidemic Transmission Dynamics Based On Two-layered Network

In the process of disease spread,it is also accompanied by the spread of diseaserelated information.In the real world,diseases are usually spread through real contact networks established by acquaintances.Disease-related information is more spread through virtual social networks.Modeling the interaction between the spread of disease-related information and the spread of disease has become a challenging subject in complex network transmission dynamics,which has attracted the attention of many scholars.Some valuable results have been obtained,which have certain guiding significance for disease prevention and control.In recent years,many models have adopted a two-layered network to model,discuss the interaction between information diffusion and disease transmission,and derive disease outbreak thresholds through mean field analysis or microscopic Markov chain method.However,in real life,due to various reason,the infected person may not update the status of the information layer in time and notify his friends on the social network of the news that he has contracted an infectious disease.At the same time,there will be individuals who know the disease-related information,but who are unwilling or fail to take effective preventive measures,which promotes the further spread of the disease.In response to the above phenomena,this paper establishes a two-layered network transmission dynamics model to study information diffusion and disease transmission,and analyzes the transmission phenomenon in depth to explore the coupling dynamics of information diffusion and disease transmission in the two-layered network.The main innovative work of the thesis includes the following two parts:? The UAU-SIS(unaware/aware/unaware-susceptible/infected/susceptible)model of partially mapping static network is proposed to study the interaction of information diffusion and disease transmission in static networks.One layer represents the diffusion network of disease-related information,while the other layer is used as the network for disease transmission;at the same time,part of nodes in the model have a mapping relationship between the two layers.If the node in two-layered network does not have mapping relationship,information and diseases will be spread independently at their respective levels.In this paper,the correspondence rate is used to describe the percentage of nodes that have a mapping relationship in the nodes,and then the model is studied according to the micro-Markov chain(MMC)method,and the threshold analytic formula of the model is obtained.A large number of experiments are conducted to verify the effectiveness of the method,and it is obtained that the corresponding rate has an impact on the disease outbreak threshold and the scale of disease transmission.Experimental results show that an increase in the corresponding rate can increase the outbreak threshold of the disease and reduce the spread scale of the disease.? The UAU-SIS model of partial mapping time-varying network is proposed to study the influence of information diffusion on the spread of disease in the network whose network topology changes with time.The model consists of two layers of networks,one of which represents the diffusion network of disease-related information,and the other represents the disease transmission network.Considering the time-varying characteristics of the network,the activity-driven network model is used to construct networks.First,the disease outbreak threshold of the model is obtained by the MMC method and Monte Carlo(MC)simulation is used to verify the effectiveness of the method.Secondly,the experiment shows that when the correspondence rate is low,the heterogeneity of the activity level of the information layer node has little effect on disease transmission;in the case of different correspondence rates,the heterogeneity of the activity level of the disease layer node will affect the disease transmission.Finally,the correlation between the activity levels of nodes in the information layer and the disease layer will also affect the scale of the outbreak of infectious diseases.The stronger the positive correlation,the more beneficial it is to suppress the spread of the disease.