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Monitoring Italian COVID-19 spread by a forced SEIRD model.

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This study introduces a flexible mathematical model (fSEIRD) to accurately forecast COVID-19 spread in Italian regions. The model effectively predicts epidemic trends, aiding public health response strategies.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • The COVID-19 pandemic necessitates accurate models for understanding and predicting disease spread.
  • Existing models require adaptation to account for real-world interventions like movement restrictions.

Purpose of the Study:

  • To develop and validate a flexible mathematical model for analyzing and forecasting COVID-19 transmission in Italian regions.
  • To adapt the forced Susceptible-Exposed-Infected-Recovered-Dead (fSEIRD) model with time-dependent infection rates.

Main Methods:

  • Utilized the Italian Protezione Civile COVID-19 data starting from 24/02/2020.
  • Proposed two piecewise time-dependent infection rate functions within the fSEIRD framework.
  • Applied the adapted models to the Lombardia and Emilia-Romagna regions.

Main Results:

  • The proposed fSEIRD models demonstrated high accuracy in fitting the observed epidemic data.
  • The models provided reliable predictions for COVID-19 spread in the studied regions.
  • The adapted models proved effective in scenarios with progressive movement restrictions.

Conclusions:

  • The flexible fSEIRD models are suitable for monitoring diverse epidemic scenarios.
  • The study confirms the utility of the proposed models for accurate COVID-19 analysis and forecasting.
  • This approach supports evidence-based public health decision-making during pandemics.