Identifying Influential Pandemic Regions Using Graph Signal Variation
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...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2211.05517 |