Non massive immunization to contain spreading on complex networks

Abstract

Optimal strategies for epidemic containment are focused on dismantling the contact network through effective immunization with minimal costs. However, network fragmentation is seldom accessible in practice and may present extreme side effects. In this work, we investigate the epidemic containment immunizing population fractions far below the percolation threshold. We report that moderate and weakly supervised immunizations can lead to finite epidemic thresholds of the susceptible-infected-susceptible model on scale-free networks by changing the nature of the transition from a specific-motif to a collectively driven process. Both pruning of efficient spreaders and increasing of their mutual separation are necessary for a collective activation. Fractions of immunized vertices needed to eradicate the epidemics which are much smaller than the percolation thresholds were observed for a broad spectrum of real networks considering targeted or acquaintance immunization strategies. Our work contributes for the construction of optimal containment, preserving network functionality through non massive and viable immunization strategies.

10 pages, 9 figures

References in corpus (11)

• Critical phenomena in complex networks
• Prediction and predictability of global epidemics: the role of the airline transportation network
• Efficient Immunization Strategies for Computer Networks and Populations
• Statistical physics of vaccination
• Dynamics of person-to-person interactions from distributed RFID sensor networks
• Thresholds for epidemic spreading in networks
• Epidemic thresholds of the Susceptible-Infected-Susceptible model on networks: A comparison of numerical and theoretical results
• Efficient local strategies for vaccination and network attack
• Immunization of Real Complex Communication Networks
• Breaking of the site-bond percolation universality in networks
• Scale-free networks resistant to intentional attacks