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Statistical and Network-Based Analysis of Italian COVID-19 Data: Communities Detection and Temporal Evolution

Author

Listed:
  • Marianna Milano

    (Data Analytics Research Center, Department of Medical and Surgical Sciences, University of Catanzaro, 88100 Catanzaro, Italy
    These authors contributed equally to this work.)

  • Mario Cannataro

    (Data Analytics Research Center, Department of Medical and Surgical Sciences, University of Catanzaro, 88100 Catanzaro, Italy
    These authors contributed equally to this work.)

Abstract

The coronavirus disease (COVID-19) outbreak started in Wuhan, China, and it has rapidly spread across the world. Italy is one of the European countries most affected by COVID-19, and it has registered high COVID-19 death rates and the death toll. In this article, we analyzed different Italian COVID-19 data at the regional level for the period 24 February to 29 March 2020. The analysis pipeline includes the following steps. After individuating groups of similar or dissimilar regions with respect to the ten types of available COVID-19 data using statistical test, we built several similarity matrices. Then, we mapped those similarity matrices into networks where nodes represent Italian regions and edges represent similarity relationships (edge length is inversely proportional to similarity). Then, network-based analysis was performed mainly discovering communities of regions that show similar behavior. In particular, network-based analysis was performed by running several community detection algorithms on those networks and by underlying communities of regions that show similar behavior. The network-based analysis of Italian COVID-19 data is able to elegantly show how regions form communities, i.e., how they join and leave them, along time and how community consistency changes along time and with respect to the different available data.

Suggested Citation

  • Marianna Milano & Mario Cannataro, 2020. "Statistical and Network-Based Analysis of Italian COVID-19 Data: Communities Detection and Temporal Evolution," IJERPH, MDPI, vol. 17(12), pages 1-64, June.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:12:p:4182-:d:370474
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    Citations

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    Cited by:

    1. Lukas Zenk & Gerald Steiner & Miguel Pina e Cunha & Manfred D. Laubichler & Martin Bertau & Martin J. Kainz & Carlo Jäger & Eva S. Schernhammer, 2020. "Fast Response to Superspreading: Uncertainty and Complexity in the Context of COVID-19," IJERPH, MDPI, vol. 17(21), pages 1-13, October.
    2. Giuseppe Agapito & Chiara Zucco & Mario Cannataro, 2020. "COVID-WAREHOUSE: A Data Warehouse of Italian COVID-19, Pollution, and Climate Data," IJERPH, MDPI, vol. 17(15), pages 1-22, August.

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