Diseases on complex networks. Modeling from a database and a protection strategy proposal

TL;DR

This paper evaluates the effectiveness of the DIL-W^{ ext{α}} ranking method for immunizing nodes in a real-world, edge-weighted network modeled for COVID-19 spread using the SIR model, proposing a targeted protection strategy.

Contribution

It introduces a novel application of the DIL-W^{ ext{α}} ranking for node immunization in weighted networks based on real data and models disease spread with the SIR framework.

Findings

DIL-W^{ ext{α}}} ranking effectively identifies critical nodes for immunization.

Targeted immunization based on DIL-W^{ ext{α}}} reduces disease spread.

The approach demonstrates practical utility in real-world network scenarios.

Abstract

Among the diverse and important applications that networks currently have is the modeling of infectious diseases. Immunization, or the process of protecting nodes in the network, plays a key role in stopping diseases from spreading. Hence the importance of having tools or strategies that allow the solving of this challenge. In this work, we evaluate the effectiveness of the DIL-W^{\alpha} ranking in immunizing nodes in an edge-weighted network. The network is obtained from a real database and the spread of COVID-19 was modeled with the classic SIR model. We apply the protection to the network, according to the importance ranking list produced by DIL-W^{\alpha}.