Infection-induced Cascading Failures -- Impact and Mitigation

TL;DR

This paper introduces a dynamic message-passing framework to analyze and mitigate cascading failures caused by infection spreading in interconnected networks, improving efficiency over traditional simulation methods.

Contribution

It develops a novel dynamic message-passing approach for studying intertwined spreading processes and proposes an optimization algorithm for failure mitigation.

Findings

Efficient inference of system states in complex spreading processes.

Identification of critical factors influencing cascading failures.

Effective mitigation strategies for network resilience.

Abstract

In the context of epidemic spreading, many intricate dynamical patterns can emerge due to the cooperation of different types of pathogens or the interaction between the disease spread and other failure propagation mechanism. To unravel such patterns, simulation frameworks are usually adopted, but they are computationally demanding on big networks and subject to large statistical uncertainty. Here, we study the two-layer spreading processes on unidirectionally dependent networks, where the spreading infection of diseases or malware in one layer can trigger cascading failures in another layer and lead to secondary disasters, e.g., disrupting public services, supply chains, or power distribution. We utilize a dynamic message-passing method to devise efficient algorithms for inferring the system states, which allows one to investigate systematically the nature of complex intertwined spreading processes and evaluate their impact. Based on such dynamic message-passing framework and optimal control, we further develop an effective optimization algorithm for mitigating network failures.