Suppressing traffic-driven epidemic spreading by adaptive routing strategy

TL;DR

This paper introduces an adaptive routing strategy that combines topological distance and local epidemic data to optimize epidemic control and traffic flow in networks, outperforming static routing schemes.

Contribution

It proposes a tunable adaptive routing method that enhances epidemic threshold and reduces congestion, providing new insights into traffic-driven epidemic management.

Findings

Optimal routing parameter $h$ maximizes epidemic threshold.

Adaptive routing outperforms static shortest path routing.

Significant congestion relief at finite node capacity.

Abstract

The design of routing strategies for traffic-driven epidemic spreading has received increasing attention in recent years. In this paper, we propose an adaptive routing strategy that incorporates topological distance with local epidemic information through a tunable parameter $h$. In the case where the traffic is free of congestion, there exists an optimal value of routing parameter $h$, leading to the maximal epidemic threshold. This means that epidemic spreading can be more effectively controlled by adaptive routing, compared to that of the static shortest path routing scheme. Besides, we find that the optimal value of $h$ can greatly relieve the traffic congestion in the case of finite node-delivering capacity. We expect our work to provide new insights into the effects of dynamic routings on traffic-driven epidemic spreading.