A simple SIR model with a large set of asymptomatic infectives

There is increasing evidence that one of the most difficult problems in trying to control the ongoing COVID-19 epidemic is the presence of a large cohort of asymptomatic infectives. We develop a SIR-type model taking into account the presence of asymptomatic, or however undetected, infective, and th...

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Main Author: Giuseppe Gaeta
Format: Article
Language:English
Published: AIMS Press 2021-03-01
Series:Mathematics in Engineering
Subjects:
Online Access:http://www.aimspress.com/article/doi/10.3934/mine.2021013?viewType=HTML
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author Giuseppe Gaeta
author_facet Giuseppe Gaeta
author_sort Giuseppe Gaeta
collection DOAJ
description There is increasing evidence that one of the most difficult problems in trying to control the ongoing COVID-19 epidemic is the presence of a large cohort of asymptomatic infectives. We develop a SIR-type model taking into account the presence of asymptomatic, or however undetected, infective, and the substantially long time these spend being infective and not isolated. We discuss how a SIR-based prediction of the epidemic course based on early data but not taking into account the presence of a large set of asymptomatic infectives would give wrong estimate of very relevant quantities such as the need of hospital beds, the time to the epidemic peak, and the number of people which are left untouched by the first wave and thus in danger in case of a second epidemic wave. In the second part of the note, we apply our model to the COVID-19 epidemics in Northern Italy. We obtain a good agreement with epidemiological data; according to the best fit of epidemiological data in terms of this model, only 10% of infectives in Italy is symptomatic.
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spelling doaj.art-f9300f1b6ee14fab859e5ad280172e162022-12-21T22:22:54ZengAIMS PressMathematics in Engineering2640-35012021-03-013213910.3934/mine.2021013A simple SIR model with a large set of asymptomatic infectivesGiuseppe Gaeta01. Dipartimento di Matematica, Università degli Studi di Milano, via Saldini 50, I-20133 Milano, Italy 2. SMRI, 00058 Santa Marinella, ItalyThere is increasing evidence that one of the most difficult problems in trying to control the ongoing COVID-19 epidemic is the presence of a large cohort of asymptomatic infectives. We develop a SIR-type model taking into account the presence of asymptomatic, or however undetected, infective, and the substantially long time these spend being infective and not isolated. We discuss how a SIR-based prediction of the epidemic course based on early data but not taking into account the presence of a large set of asymptomatic infectives would give wrong estimate of very relevant quantities such as the need of hospital beds, the time to the epidemic peak, and the number of people which are left untouched by the first wave and thus in danger in case of a second epidemic wave. In the second part of the note, we apply our model to the COVID-19 epidemics in Northern Italy. We obtain a good agreement with epidemiological data; according to the best fit of epidemiological data in terms of this model, only 10% of infectives in Italy is symptomatic.http://www.aimspress.com/article/doi/10.3934/mine.2021013?viewType=HTMLcovidepidemiological modelssir modelasymptomatic transmissionnonlinear dynamics
spellingShingle Giuseppe Gaeta
A simple SIR model with a large set of asymptomatic infectives
Mathematics in Engineering
covid
epidemiological models
sir model
asymptomatic transmission
nonlinear dynamics
title A simple SIR model with a large set of asymptomatic infectives
title_full A simple SIR model with a large set of asymptomatic infectives
title_fullStr A simple SIR model with a large set of asymptomatic infectives
title_full_unstemmed A simple SIR model with a large set of asymptomatic infectives
title_short A simple SIR model with a large set of asymptomatic infectives
title_sort simple sir model with a large set of asymptomatic infectives
topic covid
epidemiological models
sir model
asymptomatic transmission
nonlinear dynamics
url http://www.aimspress.com/article/doi/10.3934/mine.2021013?viewType=HTML
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