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COVID-WAREHOUSE: A Data Warehouse of Italian COVID-19, Pollution, and Climate Data

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  • Giuseppe Agapito

    (Department of Legal, Economic and Social Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy
    Data Analytics Research Center, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy)

  • Chiara Zucco

    (Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy)

  • Mario Cannataro

    (Data Analytics Research Center, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy
    Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy)

Abstract

The management of the COVID-19 pandemic presents several unprecedented challenges in different fields, from medicine to biology, from public health to social science, that may benefit from computing methods able to integrate the increasing available COVID-19 and related data (e.g., pollution, demographics, climate, etc.). With the aim to face the COVID-19 data collection, harmonization and integration problems, we present the design and development of COVID-WAREHOUSE, a data warehouse that models, integrates and stores the COVID-19 data made available daily by the Italian Protezione Civile Department and several pollution and climate data made available by the Italian Regions. After an automatic ETL (Extraction, Transformation and Loading) step, COVID-19 cases, pollution measures and climate data, are integrated and organized using the Dimensional Fact Model, using two main dimensions: time and geographical location. COVID-WAREHOUSE supports OLAP (On-Line Analytical Processing) analysis, provides a heatmap visualizer, and allows easy extraction of selected data for further analysis. The proposed tool can be used in the context of Public Health to underline how the pandemic is spreading, with respect to time and geographical location, and to correlate the pandemic to pollution and climate data in a specific region. Moreover, public decision-makers could use the tool to discover combinations of pollution and climate conditions correlated to an increase of the pandemic, and thus, they could act in a consequent manner. Case studies based on data cubes built on data from Lombardia and Puglia regions are discussed. Our preliminary findings indicate that COVID-19 pandemic is significantly spread in regions characterized by high concentration of particulate in the air and the absence of rain and wind, as even stated in other works available in literature.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:15:p:5596-:d:393909
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    References listed on IDEAS

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    1. Erik Brynjolfsson & John J. Horton & Adam Ozimek & Daniel Rock & Garima Sharma & Hong-Yi TuYe, 2020. "COVID-19 and Remote Work: An Early Look at US Data," NBER Working Papers 27344, National Bureau of Economic Research, Inc.
    2. 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.
    3. Behrouz Pirouz & Sina Shaffiee Haghshenas & Behzad Pirouz & Sami Shaffiee Haghshenas & Patrizia Piro, 2020. "Development of an Assessment Method for Investigating the Impact of Climate and Urban Parameters in Confirmed Cases of COVID-19: A New Challenge in Sustainable Development," IJERPH, MDPI, vol. 17(8), pages 1-17, April.
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    1. David Meintrup & Martina Nowak-Machen & Stefan Borgmann, 2021. "Nine Months of COVID-19 Pandemic in Europe: A Comparative Time Series Analysis of Cases and Fatalities in 35 Countries," IJERPH, MDPI, vol. 18(12), pages 1-17, June.

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