Community-based Immunization Strategies for Epidemic Control

TL;DR

This paper introduces community-based immunization strategies that require only local community information, effectively controlling epidemics in networks where global structure data is unavailable.

Contribution

It proposes novel community-level immunization strategies that match the efficiency of global strategies using limited network information.

Findings

Community structure significantly influences epidemic dynamics.

Community-based strategies are as effective as global centrality methods.

Proposed strategies outperform local random walk-based methods.

Abstract

Understanding the epidemic dynamics, and finding out efficient techniques to control it, is a challenging issue. A lot of research has been done on targeted immunization strategies, exploiting various global network topological properties. However, in practice, information about the global structure of the contact network may not be available. Therefore, immunization strategies that can deal with a limited knowledge of the network structure are required. In this paper, we propose targeted immunization strategies that require information only at the community level. Results of our investigations on the SIR epidemiological model, using a realistic synthetic benchmark with controlled community structure, show that the community structure plays an important role in the epidemic dynamics. An extensive comparative evaluation demonstrates that the proposed strategies are as efficient as the most influential global centrality based immunization strategies, despite the fact that they use a limited amount of information. Furthermore, they outperform alternative local strategies, which are agnostic about the network structure, and make decisions based on random walks.