Traffic Optimization to Control Epidemic Outbreaks in Metapopulation Models

TL;DR

This paper introduces a spectral-based convex optimization framework to optimize traffic control in metapopulation models, aiming to contain epidemic outbreaks by adjusting inter-city traffic flows.

Contribution

It presents a novel spectral condition for epidemic containment and develops a convex optimization approach for cost-effective traffic control in metapopulation networks.

Findings

Spectral conditions effectively characterize epidemic spread.

Convex optimization provides cost-efficient traffic control strategies.

Framework applicable to large-scale urban networks.

Abstract

We propose a novel framework to study viral spreading processes in metapopulation models. Large subpopulations (i.e., cities) are connected via metalinks (i.e., roads) according to a metagraph structure (i.e., the traffic infrastructure). The problem of containing the propagation of an epidemic outbreak in a metapopulation model by controlling the traffic between subpopulations is considered. Controlling the spread of an epidemic outbreak can be written as a spectral condition involving the eigenvalues of a matrix that depends on the network structure and the parameters of the model. Based on this spectral condition, we propose a convex optimization framework to find cost-optimal approaches to traffic control in epidemic outbreaks.