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

COVID-19 Hospitalisation in Portugal, the first year: Results from hospital discharge data

“…We took advantage of the DRG data to explore time in hospital care and other important parameters providing the necessary tools and knowledge for the construction of better COVID-19 models that consider hospitalization. The parameters here presented were already used in the COVID-19 in-CTRL model [2].…”

“…From then onwards, several periods of confinement were implemented, according to the rise and fall in the number of cases and other epidemiological factors [2,6].…”

“…We took advantage of the DRG data to explore time in hospital care and other important parameters providing the necessary tools and knowledge for the construction of better COVID-19 models that consider hospitalization. The parameters here presented were already used in the COVID-19 in-CTRL model[2].ACKNOWLEDGMENTSThe authors acknowledge financial support from the Fundação para a Ciência e Tecnologia-FCT through project 692 2ª edição Research 4 covid, project name Projeção do Impacte das medidas Não-farmacológicas de Controlo e mitigação da epidemia de COVID-19 em Tempo ReaL (COVID-19 in-CTRL). The first author also acknowledges FCT within the grant with reference "COVID inCTRL/DEP/01/2020".…”

The Multi-Compartment SI(RD) Model with Regime Switching: An Application to COVID-19 Pandemic

“…Some of the concepts presented in the following are classical concepts in the infectious disease modelling literature. Variants of the SIR model were considered by one of the authors in [24] and, for the COVID-19 epidemic context, in [25,26]. In the following, we will focus on the main differences between previous models and the one we study in this work.…”

“…In Section 4.5, we will consider the use of some secondary parameters to overcome this issue. 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 3, 6, 3, 8, 10, 10, 17, 16,24,19,21,20,27,22,37,20,29,16,34,35,29,26,35,34,31,32,32,30,28,30,27,21,27,23,35,34,26,23,25,20,25,16,18,16,20,20,11,15,16,9,12,9,9,19,12,9,6,13,15,…”