Virus Spreading And Immunization Strategy On Interconnected Networks

With the acceleration of the process and development in a global network, the connections of people and the flows of goods are becoming frequently increased, so that the diffusion rate of the disease is greatly increased. To against the spread of the epidemic, people made a variety of models to study the spread of epidemics and analyze the spread of disease, including the dissemination mechanisms, propagation characteristics, and prevention and control, etc., and made a lot of new progress in these areas. It was found that in real life, many networks are not single networks, and they have more or less contact with other networks, which will lead to the virus easier to spread than in a single network. With the further study of complex networks, network structure and network interactions in the communication process between the impact has also been more and more attention. For example, with the virus spreading, the reaction of most people is to avoid contact with infected persons, this behavior leads to change the network structure; and in turn, the changes in the structure of networks will lead to change the node states in the network.In this paper, based on the interconnected network topology, we consider a network that an individual has a certain awareness and can avoid to connected with infected individuals. Then we propose the virus propagation model on the interconnected network, discuss the impact of the interconnected network topology and the avoidance behavior of the virus spread, and compare the result with the cases in a single network. Meanwhile, considering the characteristics of different nodes in interconnected network, we propose a immunization strategy based on local centerality of different nodes, analysis the effect of immunization strategies based on local centerality in different network structures. The results show that if the propogation rate is larger than the threshold, the structure of interconnected network has little effect on the transmission of virus. Only w hen the propogation rate is smaller than the threshold, the connection between two networks will promote the spread of virus. Meanwhile, the study found that individuals continue to strengthen risk awareness and avoidance behavior can slow down the spread of the virus spreading, but does not completely prevent the spreading of the virus. Finally, through the simulation experiments, it can be verifed that in different structures of networks, the immunization strategies based on local centrality are more effective in inhibiting the spread of the virus.