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A New Parallel Methodology for the Network Analysis of COVID-19 Data

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Euro-Par 2020: Parallel Processing Workshops (Euro-Par 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12480))

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Abstract

Coronavirus disease (COVID-19) outbreak started at Wuhan, China, and it has rapidly spread across the world. In this article, we present a new methodology for network-based analysis of Italian COVID-19 data. The methodology includes the following steps: (i) a parallel methodology to build similarity matrices that represent similar or dissimilar regions with respect to data; (ii) the mapping of similarity matrices into networks where nodes represent Italian regions, and edges represent similarity relationships; (iii) the discovering communities of regions that show similar behaviour. The methodology is general and can be applied to world-wide data about COVID-19. Experiments was performed on real datasets about Italian regions, and they although the limited size of the Italian COVID-19 dataset, a quite linear speed-up was obtained up to six cores.

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Author information

Authors and Affiliations

  1. Data Analytics Research Center, Magna Græcia University, 88100, Catanzaro, Italy

    Giuseppe Agapito, Marianna Milano & Mario Cannataro

  2. Department of Medical and Surgical Sciences, Magna Græcia University, 88100, Catanzaro, Italy

    Marianna Milano & Mario Cannataro

  3. Department of Legal, Economic and Social Sciences, Magna Græcia University, 88100, Catanzaro, Italy

    Giuseppe Agapito

Authors
  1. Giuseppe Agapito
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  2. Marianna Milano
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  3. Mario Cannataro
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Corresponding author

Correspondence to Marianna Milano .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Bartosz Balis

  2. CiTIUS, Santiago de Compostela, Spain

    Dora B. Heras

  3. ICAR-CNR, Naples, Italy

    Laura Antonelli

  4. University of Stirling, Stirling, UK

    Andrea Bracciali

  5. Friedrich-Alexander-Universität, Erlangen, Germany

    Thomas Gruber

  6. Konkuk University, Seoul, Korea (Republic of)

    Jin Hyun-Wook

  7. Otto von Guericke University Magdeburg, Magdeburg, Germany

    Michael Kuhn

  8. Tennessee Tech University, Cookeville, TN, USA

    Stephen L. Scott

  9. Koç University, Istanbul, Turkey

    Didem Unat

  10. Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

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Agapito, G., Milano, M., Cannataro, M. (2021). A New Parallel Methodology for the Network Analysis of COVID-19 Data. In: Balis, B., et al. Euro-Par 2020: Parallel Processing Workshops. Euro-Par 2020. Lecture Notes in Computer Science(), vol 12480. Springer, Cham. https://doi.org/10.1007/978-3-030-71593-9_26

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  • DOI: https://doi.org/10.1007/978-3-030-71593-9_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71592-2

  • Online ISBN: 978-3-030-71593-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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