Analysis of epidemic vaccination strategies by node importance and evolutionary game on complex networks

Currently, vaccination is the most effective means to prevent the spread of infectious diseases. In this paper, a novel SIRV-NI-EG (susceptible, infected, recovered, vaccinated - node importance - evolutionary game) model is established to analyze the evolution of vaccination strategy under the combination of mandatory vaccination and voluntary vaccination. For the mandatory vaccination, some nodes with high node importance are firstly vaccinated in a certain proportion according to the node importance ranking. The remaining nodes in the network voluntarily decide whether to vaccinate according to the surrounding situation based on the evolutionary game theory. And degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, PageRank, k-core, structural holes and WTOPSIS are used to evaluate the node importance in the network. In addition, the methods based on node deletion are used to further evaluate the importance of the initial vaccination nodes. Finally, vaccination evolutionary game analysis based on the SIRV-NI-EG model is performed on three complex networks, including USAir network, Facebook network and BA scale-free network. The results show that the performances of all evaluation indicators are better than random vaccination. Our conclusions can provide better vaccination strategies for government decision-making to control the spread of infectious diseases.