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

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...

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Autores principales:	Erminia Antonelli, Elena Loli Piccolomini, Fabiana Zama
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2022
Materias:	Compartmental model with vaccine SEIRDV Switched model Hybrid model Forcing function Model calibration Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/d9a2ba6fdd58461dbbfdcc0810b2e3a2
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spelling	oai:doaj.org-article:d9a2ba6fdd58461dbbfdcc0810b2e3a22021-11-20T05:08:10ZSwitched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy2468-042710.1016/j.idm.2021.11.001https://doaj.org/article/d9a2ba6fdd58461dbbfdcc0810b2e3a22022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2468042721000695https://doaj.org/toc/2468-0427This 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.Erminia AntonelliElena Loli PiccolominiFabiana ZamaKeAi Communications Co., Ltd.articleCompartmental model with vaccineSEIRDVSwitched modelHybrid modelForcing functionModel calibrationInfectious and parasitic diseasesRC109-216ENInfectious Disease Modelling, Vol 7, Iss 1, Pp 1-15 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Compartmental model with vaccine SEIRDV Switched model Hybrid model Forcing function Model calibration Infectious and parasitic diseases RC109-216
spellingShingle	Compartmental model with vaccine SEIRDV Switched model Hybrid model Forcing function Model calibration Infectious and parasitic diseases RC109-216 Erminia Antonelli Elena Loli Piccolomini Fabiana Zama Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy
description	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.
format	article
author	Erminia Antonelli Elena Loli Piccolomini Fabiana Zama
author_facet	Erminia Antonelli Elena Loli Piccolomini Fabiana Zama
author_sort	Erminia Antonelli
title	Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy
title_short	Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy
title_full	Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy
title_fullStr	Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy
title_full_unstemmed	Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy
title_sort	switched forced seirdv compartmental models to monitor covid-19 spread and immunization in italy
publisher	KeAi Communications Co., Ltd.
publishDate	2022
url	https://doaj.org/article/d9a2ba6fdd58461dbbfdcc0810b2e3a2
work_keys_str_mv	AT erminiaantonelli switchedforcedseirdvcompartmentalmodelstomonitorcovid19spreadandimmunizationinitaly AT elenalolipiccolomini switchedforcedseirdvcompartmentalmodelstomonitorcovid19spreadandimmunizationinitaly AT fabianazama switchedforcedseirdvcompartmentalmodelstomonitorcovid19spreadandimmunizationinitaly
_version_	1718419571459751936

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

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