Modeling Of Virus Propagation On Multiple Networks

As we have entered the information age,the effect of the information proliferation on the spread of the disease could’t be ignored.Statistics show that the amount of information obtained from microblogging,Facebook,Twitter and other social networking platform have accounted for more than 90% of the total information,indicating that social network has become the main channel for people to obtain information.The transmission of virus generally relys on the real contact network to complete the spread.The topological characteristics and the transmission pathways of the social and contact network are different,so research on the interactive process of information virus on multiple networks is of great significance.In this paper,the model of interactive propagation between information and virus on multiple networks is proposed to study the effect of information on virus transmission.Firstly,based on the information interaction model of multiple networks,this paper considers that the individual will choose the vaccination according to the environment in real life,and put forward the crisis awareness vaccination scheme according to the number of infection of neighbor nodes on multiple networks.By studying the factors influencing the spread of disease and the threshold of disease transmission,we can found that the effect of information on the scale-free network was more obvious than that of the small-world network.This also shows that with the popularity of social networking sites,people are increasingly strong ability to control the disease.Compared with Wang’s SIR-SIRV model,it shows that the vaccination program proposed in this paper is more advantageous,more flexible and closer to real life.Secondly,based on the information interaction model of multiple networks,considering the limited time of treatment of disease,this paper proposes a virus transmission model based on limited treatment time on multiple networks.The simulation results show that the death density of the individual is large when the treatment time is small and the density of the dead individual approaches zero when the treatment time is greater than a certain value.This shows that when the check frequency is greater than a specific frequency.This means that when the check frequency is greater than a certain frequency,you can detect the virus in time to be treated.At the same time,we also take the heterogeneity of the treatment time at the population level into consideration in this model.It can be find in this model that the heterogeneity of the treatment time had different effects on the transmission of the virus when the effective treatment time was different.Thirdly,on the basis of information and virus interaction propagation on multiple network,the model of virus interaction are put forward when we take the information attenuation into account which mainly refers to the information loss and propagation of information.With the mean field method that used to unify the information and virus transmission process,we have deduced the threshold of virus transmission in this paper.It shows that the virus transmission threshold is not only related to the transmission of the virus but also related to the information quantity of the individual.It also shows that the threshold of the virus propagation is independent of the information generation probability,and the attenuation of the information will lead to a decrease in the virus propagation threshold.By comparing the Simulation results carried out on artificial network and real social network,it shows that the propagation model of this paper is effective.Finally,the influence of information on virus propagation has been studied by comparing the results of simulation verify the result theoretical derivation.