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Monitoring Italian COVID-19 spread by a forced SEIRD model

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  • Elena Loli Piccolomini
  • Fabiana Zama

Abstract

Due to the recent evolution of the COVID-19 outbreak, the scientific community is making efforts to analyse models for understanding the present situation and for predicting future scenarios. In this paper, we propose a forced Susceptible-Exposed-Infected-Recovered-Dead (fSEIRD) differential model for the analysis and forecast of the COVID-19 spread in Italian regions, using the data from the Italian Protezione Civile (Italian Civil Protection Department) from 24/02/2020. In this study, we investigate an adaptation of fSEIRD by proposing two different piecewise time-dependent infection rate functions to fit the current epidemic data affected by progressive movement restriction policies put in place by the Italian government. The proposed models are flexible and can be quickly adapted to monitor various epidemic scenarios. Results on the regions of Lombardia and Emilia-Romagna confirm that the proposed models fit the data very accurately and make reliable predictions.

Suggested Citation

  • Elena Loli Piccolomini & Fabiana Zama, 2020. "Monitoring Italian COVID-19 spread by a forced SEIRD model," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-17, August.
    • Handle: RePEc:plo:pone00:0237417
    DOI: 10.1371/journal.pone.0237417
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    References listed on IDEAS

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    1. Lucia Russo & Cleo Anastassopoulou & Athanasios Tsakris & Gennaro Nicola Bifulco & Emilio Fortunato Campana & Gerardo Toraldo & Constantinos Siettos, 2020. "Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-22, October.
    2. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
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    1. Giuli, Francesco & Maugeri, Gabriele, 2023. "Economic Effects of Covid-19 and Non-Pharmaceutical Interventions: applying a SEIRD-Macro Model to Italy," MPRA Paper 118422, University Library of Munich, Germany.
    2. Petropoulos, Fotios & Makridakis, Spyros & Stylianou, Neophytos, 2022. "COVID-19: Forecasting confirmed cases and deaths with a simple time series model," International Journal of Forecasting, Elsevier, vol. 38(2), pages 439-452.
    3. Song, Jialu & Xie, Hujin & Gao, Bingbing & Zhong, Yongmin & Gu, Chengfan & Choi, Kup-Sze, 2021. "Maximum likelihood-based extended Kalman filter for COVID-19 prediction," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    4. Giuli, Francesco & Maugeri, Gabriele, 2022. "Economic Effects of Covid-19 and Non-Pharmaceutical Interventions: applying a SEIRD-RBC Model to Italy," MPRA Paper 114673, University Library of Munich, Germany.

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