A control measure for epidemic spread based on the susceptible-infectious-susceptible (SIS) model

When an epidemic occurs in a network, finding the important links and cutting them off is an effective measure for preventing the spread of the epidemic. Traditional methods that remove important links easily lead to a disconnected network, inevitably incurring high costs arising from quarantining individuals or communities in a real-world network. In this study, we combine the clustering coefficient and the eigenvector to identify the important links using the susceptible-infectious-susceptible (SIS) model. The results show that our approach can improve the epidemic threshold while maintaining the connectivity of the network to control the spread of the epidemic. Experiments on multiple real-world and synthetic networks of varying sizes, demonstrate the effectiveness and scalability of our approach.