Escolano, Francisco, Lozano, Miguel Angel and Hancock, Edwin R. orcid.org/0000-0003-4496-2028 (2021) The Entropy of Graph Embeddings: A Proxy of Potential Mobility in Covid19 Outbreaks. In: Torsello, Andrea, Rossi, Luca, Pelillo, Marcello, Biggio, Battista and Robles-Kelly, Antonio, (eds.) Structural, Syntactic, and Statistical Pattern Recognition. Springer , Cham , pp. 195-204.
Abstract
In this paper, we propose a proxy of the R0R0(reproductive number) of COVID-19 by computing the entropy of the mobility graph during the first peak of the pandemic. The study was performed by the COVID-19 Data Science Task Force at the Comunidad Valenciana (Spain) during 70 days. Since mobility graphs are naturally attributed, directed and become more and more disconnected as more and more non-pharmaceutical measures are implemented, we discarded spectral complexity measures and classical ones such as network efficiency. Alternatively, we turned our attention to embeddings resulting from random walks and their links with stochastic matrices. In our experiments, we show that this leads to a powerful tool for predicting the spread of the virus and to assess the effectiveness of the political interventions.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 15 Apr 2021 10:00 |
Last Modified: | 03 Dec 2024 11:08 |
Published Version: | https://doi.org/10.1007/978-3-030-73973-7_19 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-030-73973-7_19 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173120 |
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