Efficient network immunization strategy based on generalized Herfindahl–Hirschman index

Abstract

The topic of finding effective strategies to restrain epidemic spreading in complex networks is of current interest. A widely used approach for epidemic containment is the fragmentation of the contact networks through immunization. However, due to the limitation of immune resources, we cannot always fragment the contact network completely. In this study, based on the size distribution of connected components for the network, we designed a risk indicator of epidemic outbreaks, the generalized Herfindahl–Hirschman index (GHI), which measures the upper bound of the expected infection’s prevalence (the fraction of infected nodes) in random outbreaks. An immunization approach based on minimizing GHI is developed to reduce the infection risk for individuals in the network. Experimental results show that our immunization strategy could effectively decrease the infection’s prevalence as compared to other existing strategies, especially against infectious diseases with higher infection rates or lower recovery rates. The findings provide an efficient and practicable strategy for immunization against epidemic diseases.

Information transfer pathways: signal propagation in complex global topologies

“…The outbreak and spread of infectious diseases from one location to all over the world [1][2][3][4][5][6][7], the control of proteins or genes over the biochemical processes [8][9][10][11][12], and many other dynamic processes involve the propagation of perturbations from some nodes to the overall network [13], which indicates that the patterns of global propagation for small perturbations have a wide range of applications in the real world [14]. Specifically, studying the propagation time of dynamical systems can effectively monitor the speed of signal propagation, which is of great significance for research on network control and network stability [15][16][17][18][19][20][21].…”