Global efficiency of local immunization on complex networks

TL;DR

This paper compares local and global network measures for identifying influential spreaders to optimize immunization strategies across diverse real-world networks, revealing that local measures can be more effective and practical.

Contribution

It introduces a framework for selecting optimal local immunization measures based on network structure and epidemic regime, improving efficiency over global measures.

Findings

Local measures outperform global ones in many cases

Community-based measures are effective at mesoscopic scales

An analytical framework guides measure selection depending on epidemic regime

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.