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Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model

Author

Listed:
  • Luisa Ferrari

    (Department of Statistical Science, University College London, London WC1E 6BT, UK
    These authors contributed equally to this work.)

  • Giuseppe Gerardi

    (Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy
    These authors contributed equally to this work.)

  • Giancarlo Manzi

    (Department of Economics, Management and Quantitative Methods and Data Science Research Center, University of Milan, 20122 Milan, Italy
    These authors contributed equally to this work.)

  • Alessandra Micheletti

    (Department of Environmental Science and Policy and Data Science Research Center, University of Milan, 20122 Milan, Italy
    These authors contributed equally to this work.)

  • Federica Nicolussi

    (Department of Economics, Management and Quantitative Methods and Data Science Research Center, University of Milan, 20122 Milan, Italy
    These authors contributed equally to this work.)

  • Elia Biganzoli

    (Department of Clinical Sciences and Community Health and Data Science Research Center, University of Milan, 20122 Milan, Italy
    These authors contributed equally to this work.)

  • Silvia Salini

    (Department of Economics, Management and Quantitative Methods and Data Science Research Center, University of Milan, 20122 Milan, Italy
    These authors contributed equally to this work.)

Abstract

In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.

Suggested Citation

  • Luisa Ferrari & Giuseppe Gerardi & Giancarlo Manzi & Alessandra Micheletti & Federica Nicolussi & Elia Biganzoli & Silvia Salini, 2021. "Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model," IJERPH, MDPI, vol. 18(12), pages 1-20, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6563-:d:577284
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    1. Şuayip Yüzbaşı & Gamze Yıldırım, 2023. "A Pell–Lucas Collocation Approach for an SIR Model on the Spread of the Novel Coronavirus (SARS CoV-2) Pandemic: The Case of Turkey," Mathematics, MDPI, vol. 11(3), pages 1-22, January.

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