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

Author

Listed:
  • Constantino Caetano

    (Instituto Nacional de Saúde Doutor Ricardo Jorge, 1649-016 Lisbon, Portugal)

  • Maria Luísa Morgado

    (Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
    Department of Mathematics, University of Trás-os-Montes e Alto Douro, UTAD, 5001-801 Vila Real, Portugal)

  • Paula Patrício

    (Center for Mathematics and Applications (CMA), FCT NOVA and Department of Mathematics, FCT NOVA, Quinta da Torre, 2829-516 Caparica, Portugal)

  • João F. Pereira

    (Department of Mathematics, University of Trás-os-Montes e Alto Douro, UTAD, 5001-801 Vila Real, Portugal)

  • Baltazar Nunes

    (Instituto Nacional de Saúde Doutor Ricardo Jorge, 1649-016 Lisbon, Portugal
    Centro de Investigação em Saúde Pública, Escola Nacional de Saúde Pública, Universidade NOVA de Lisboa, 1600-560 Lisbon, Portugal)

Abstract

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 ( R ( t ) ) 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 R ( t ) = 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.

Suggested Citation

  • Constantino Caetano & Maria Luísa Morgado & Paula Patrício & João F. Pereira & Baltazar Nunes, 2021. "Mathematical Modelling of the Impact of Non-Pharmacological Strategies to Control the COVID-19 Epidemic in Portugal," Mathematics, MDPI, vol. 9(10), pages 1-16, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:10:p:1084-:d:552626
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    References listed on IDEAS

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    1. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2011. "DEoptim: An R Package for Global Optimization by Differential Evolution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i06).
    2. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
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