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COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data.
Milano, Marianna; Zucco, Chiara; Cannataro, Mario.
  • Milano M; Department of Medical and Surgical Sciences, University of Catanzaro, Catanzaro, 88100 Italy.
  • Zucco C; Data Analytics Research Center, University of Catanzaro, Catanzaro, Catanzaro, 88100 Italy.
  • Cannataro M; Department of Medical and Surgical Sciences, University of Catanzaro, Catanzaro, 88100 Italy.
Netw Model Anal Health Inform Bioinform ; 10(1): 46, 2021.
Article in English | MEDLINE | ID: covidwho-1303386
ABSTRACT
Understanding the evolution of the spread of the COVID-19 pandemic requires the analysis of several data at the spatial and temporal levels. Here, we present a new network-based methodology to analyze COVID-19 data measures containing spatial and temporal features and its application on a real dataset. The goal of the methodology is to analyze sets of homogeneous datasets (i.e. COVID-19 data taken in different periods and in several regions) using a statistical test to find similar/dissimilar datasets, mapping such similarity information on a graph and then using a community detection algorithm to visualize and analyze the spatio-temporal evolution of data. We evaluated diverse Italian COVID-19 data made publicly available by the Italian Protezione Civile Department at https//github.com/pcm-dpc/COVID-19/. Furthermore, we considered the climate data related to two periods and we integrated them with COVID-19 data measures to detect new communities related to climate changes. In conclusion, the application of the proposed methodology provides a network-based representation of the COVID-19 measures by highlighting the different behaviour of regions with respect to pandemics data released by Protezione Civile and climate data. The methodology and its implementation as R function are publicly available at https//github.com/mmilano87/analyzeC19D.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies Language: English Journal: Netw Model Anal Health Inform Bioinform Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies Language: English Journal: Netw Model Anal Health Inform Bioinform Year: 2021 Document Type: Article