This paper presents a new hybrid compartmental model for studying the COVID -19 epidemic evolution in Italy since the beginning of the vaccination campaign started on 2020/12/27 and shows forecasts of the epidemic evolution in Italy in the first six months. The proposed compartmental model subdivides the population into six compartments and extends the SEIRD model proposed in [E.L. Piccolomini and F. Zama , PLOS ONE, 15(8): 1–17 , 08 2020] by adding the vaccinated population and framing the global model as a hybrid-switched dynamical system. Aiming to represent the quantities that characterize the epidemic behaviour from an accurate fit to the observed data, we partition the observation time interval into sub-intervals. The model parameters change according to a switching rule depending on the data behaviour and the infection rate continuity condition. In particular, we study the representation of the infection rate both as linear and exponential piecewise continuous functions. We choose the length of sub-intervals balancing the data fit with the model complexity through the Bayesian Information Criterion. We tested the model on Italian data and on local data from Emilia - Romagna region. The calibration of the model shows an excellent representation of the epidemic behaviour in both cases. Thirty days forecasts have proven to well reproduce the infection spread, better for regional than for national data. Both models produce accurate predictions of infected, but the exponential-based one perform better in most of the cases. Finally, we discuss different possible forecast scenarios obtained by simulating an increased vaccination rate.

Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy

Antonelli, Erminia;Piccolomini, Elena Loli;Zama, Fabiana
2022

Abstract

This paper presents a new hybrid compartmental model for studying the COVID -19 epidemic evolution in Italy since the beginning of the vaccination campaign started on 2020/12/27 and shows forecasts of the epidemic evolution in Italy in the first six months. The proposed compartmental model subdivides the population into six compartments and extends the SEIRD model proposed in [E.L. Piccolomini and F. Zama , PLOS ONE, 15(8): 1–17 , 08 2020] by adding the vaccinated population and framing the global model as a hybrid-switched dynamical system. Aiming to represent the quantities that characterize the epidemic behaviour from an accurate fit to the observed data, we partition the observation time interval into sub-intervals. The model parameters change according to a switching rule depending on the data behaviour and the infection rate continuity condition. In particular, we study the representation of the infection rate both as linear and exponential piecewise continuous functions. We choose the length of sub-intervals balancing the data fit with the model complexity through the Bayesian Information Criterion. We tested the model on Italian data and on local data from Emilia - Romagna region. The calibration of the model shows an excellent representation of the epidemic behaviour in both cases. Thirty days forecasts have proven to well reproduce the infection spread, better for regional than for national data. Both models produce accurate predictions of infected, but the exponential-based one perform better in most of the cases. Finally, we discuss different possible forecast scenarios obtained by simulating an increased vaccination rate.
Antonelli, Erminia; Piccolomini, Elena Loli; Zama, Fabiana
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S2468042721000695-main.pdf

accesso aperto

Descrizione: Modello SEIRDV (parte 2) risultati sperimentali della calibrazione (parte 3), risultati previsione (parte 4).
Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
Dimensione 1.82 MB
Formato Adobe PDF
1.82 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/871617
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 1
social impact