Global efficiency of local immunization on complex networks

Abstract

Epidemics occur in all shapes and forms: infections propagating in our sparse sexual networks, rumours and diseases spreading through our much denser social interactions, or viruses circulating on the Internet. With the advent of large databases and efficient analysis algorithms, these processes can be better predicted and controlled. In this study, we use different characteristics of network organization to identify the influential spreaders in 17 empirical networks of diverse nature using 2 epidemic models. We find that a judicious choice of local measures, based either on the network's connectivity at a microscopic scale or on its community structure at a mesoscopic scale, compares favorably to global measures, such as betweenness centrality, in terms of efficiency, practicality and robustness. We also develop an analytical framework that highlights a transition in the characteristic scale of different epidemic regimes. This allows to decide which local measure should govern immunization in a given scenario.

15 pages and 8 figures

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• Uncovering the overlapping community structure of complex networks in nature and society
• Improving immunization strategies
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• Propagation dynamics on networks featuring complex topologies
• Fundamental statistical features and self-similar properties of tagged networks
• Bond percolation on a class of correlated and clustered random graphs

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• Community-based Immunization Strategies for Epidemic Control
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