Mathematical Modelling of the Impact of Non-Pharmacological Strategies to Control the COVID-19 Epidemic in Portugal

In this paper, we present an age-structured SEIR model that uses contact patterns to reflect the physical distance measures implemented in Portugal to control the COVID-19 pandemic. By using these matrices and proper estimates for the parameters in the model, we were able to ascertain the impact of...

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Main Authors: Constantino Caetano, Maria Luísa Morgado, Paula Patrício, João F. Pereira, Baltazar Nunes
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/9/10/1084
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spelling doaj-b5101ba61cfc42ed922e37e9a9b776ec2021-05-31T23:45:17ZengMDPI AGMathematics2227-73902021-05-0191084108410.3390/math9101084Mathematical Modelling of the Impact of Non-Pharmacological Strategies to Control the COVID-19 Epidemic in PortugalConstantino Caetano0Maria Luísa Morgado1Paula Patrício2João F. Pereira3Baltazar Nunes4Instituto Nacional de Saúde Doutor Ricardo Jorge, 1649-016 Lisbon, PortugalCenter for Computational and Stochastic Mathematics, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, PortugalCenter for Mathematics and Applications (CMA), FCT NOVA and Department of Mathematics, FCT NOVA, Quinta da Torre, 2829-516 Caparica, PortugalDepartment of Mathematics, University of Trás-os-Montes e Alto Douro, UTAD, 5001-801 Vila Real, PortugalInstituto Nacional de Saúde Doutor Ricardo Jorge, 1649-016 Lisbon, PortugalIn this paper, we present an age-structured SEIR model that uses contact patterns to reflect the physical distance measures implemented in Portugal to control the COVID-19 pandemic. By using these matrices and proper estimates for the parameters in the model, we were able to ascertain the impact of mitigation strategies employed in the past. Results show that the March 2020 lockdown had an impact on disease transmission, bringing the effective reproduction number (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">R</mi><mo>(</mo><mi>t</mi><mo>)</mo></mrow></semantics></math></inline-formula>) below 1. We estimate that there was an increase in the transmission after the initial lift of the measures on 6 May 2020 that resulted in a second wave that was curbed by the October and November measures. December 2020 saw an increase in the transmission reaching an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">R</mi><mo>(</mo><mi>t</mi><mo>)</mo></mrow></semantics></math></inline-formula> = 1.45 in early January 2021. Simulations indicate that the lockdown imposed on the 15 January 2021 might reduce the intensive care unit (ICU) demand to below 200 cases in early April if it lasts at least 2 months. As it stands, the model was capable of projecting the number of individuals in each infection phase for each age group and moment in time.https://www.mdpi.com/2227-7390/9/10/1084epidemiological modelsSEIR type compartmental modelCOVID-19mathematical modellingcontact matrices
collection DOAJ
language English
format Article
sources DOAJ
author Constantino Caetano
Maria Luísa Morgado
Paula Patrício
João F. Pereira
Baltazar Nunes
spellingShingle Constantino Caetano
Maria Luísa Morgado
Paula Patrício
João F. Pereira
Baltazar Nunes
Mathematical Modelling of the Impact of Non-Pharmacological Strategies to Control the COVID-19 Epidemic in Portugal
Mathematics
epidemiological models
SEIR type compartmental model
COVID-19
mathematical modelling
contact matrices
author_facet Constantino Caetano
Maria Luísa Morgado
Paula Patrício
João F. Pereira
Baltazar Nunes
author_sort Constantino Caetano
title Mathematical Modelling of the Impact of Non-Pharmacological Strategies to Control the COVID-19 Epidemic in Portugal
title_short Mathematical Modelling of the Impact of Non-Pharmacological Strategies to Control the COVID-19 Epidemic in Portugal
title_full Mathematical Modelling of the Impact of Non-Pharmacological Strategies to Control the COVID-19 Epidemic in Portugal
title_fullStr Mathematical Modelling of the Impact of Non-Pharmacological Strategies to Control the COVID-19 Epidemic in Portugal
title_full_unstemmed Mathematical Modelling of the Impact of Non-Pharmacological Strategies to Control the COVID-19 Epidemic in Portugal
title_sort mathematical modelling of the impact of non-pharmacological strategies to control the covid-19 epidemic in portugal
publisher MDPI AG
series Mathematics
issn 2227-7390
publishDate 2021-05-01
description In this paper, we present an age-structured SEIR model that uses contact patterns to reflect the physical distance measures implemented in Portugal to control the COVID-19 pandemic. By using these matrices and proper estimates for the parameters in the model, we were able to ascertain the impact of mitigation strategies employed in the past. Results show that the March 2020 lockdown had an impact on disease transmission, bringing the effective reproduction number (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">R</mi><mo>(</mo><mi>t</mi><mo>)</mo></mrow></semantics></math></inline-formula>) below 1. We estimate that there was an increase in the transmission after the initial lift of the measures on 6 May 2020 that resulted in a second wave that was curbed by the October and November measures. December 2020 saw an increase in the transmission reaching an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">R</mi><mo>(</mo><mi>t</mi><mo>)</mo></mrow></semantics></math></inline-formula> = 1.45 in early January 2021. Simulations indicate that the lockdown imposed on the 15 January 2021 might reduce the intensive care unit (ICU) demand to below 200 cases in early April if it lasts at least 2 months. As it stands, the model was capable of projecting the number of individuals in each infection phase for each age group and moment in time.
topic epidemiological models
SEIR type compartmental model
COVID-19
mathematical modelling
contact matrices
url https://www.mdpi.com/2227-7390/9/10/1084
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