Identifying Influential Pandemic Regions Using Graph Signal Variation

TL;DR

This paper introduces graph signal variation metrics to identify influential regions in pandemic spread, aiding targeted containment strategies by analyzing infection dynamics on a network.

Contribution

It proposes novel local variation metrics on graphs to pinpoint influential regions in disease spread, improving over traditional global filtering methods.

Findings

Local variation metrics outperform global filtering in simulations

Metrics effectively identify key regions influencing disease spread

Method enhances understanding of geographical infection dynamics

Abstract

Developing methods to analyse infection spread is an important step in the study of pandemic and containing them. The principal mode for geographical spreading of pandemics is the movement of population across regions. We are interested in identifying regions (cities, states, or countries) which are influential in aggressively spreading the disease to neighboring regions. We consider a meta-population network with SIR (Susceptible-Infected-Recovered) dynamics and develop graph signal-based metrics to identify influential regions. Specifically, a local variation and a temporal local variation metric is proposed. Simulations indicate usefulness of the local variation metrics over the global graph-based processing such as filtering.