Effective Vaccination Strategies in Network-based SIR Model

TL;DR

This paper investigates various vaccination strategies within a network-based SIR epidemic model, demonstrating that combining network centrality and disease factors yields more effective containment on real-world and synthetic networks.

Contribution

It introduces a hybrid vaccination algorithm that outperforms existing strategies by integrating network structure and disease parameters.

Findings

Hybrid algorithm reduces final death ratio more effectively.

Considering both network and disease factors improves strategy performance.

Results validated on real-world and synthetic networks.

Abstract

Controlling and understanding epidemic outbreaks has recently drawn great interest in a large spectrum of research communities. Vaccination is one of the most well-established and effective strategies in order to contain an epidemic. In the present study, we investigate a network-based virus-spreading model building on the popular SIR model. Furthermore, we examine the efficacy of various vaccination strategies in preventing the spread of infectious diseases and maximizing the survival ratio. The experimented strategies exploit a wide range of approaches such as relying on network structure centrality measures, focusing on disease-spreading parameters, and a combination of both. Our proposed hybrid algorithm, which combines network centrality and illness factors, is found to perform better than previous strategies in terms of lowering the final death ratio in the community on various real-world networks and synthetic graph models. Our findings particularly emphasize the significance of taking both network structure properties and disease characteristics into account when devising effective vaccination strategies.