Optimal control of the COVID-19 pandemic: controlled sanitary deconfinement in Portugal

Abstract

The COVID-19 pandemic has forced policy makers to decree urgent confinements to stop a rapid and massive contagion. However, after that stage, societies are being forced to find an equilibrium between the need to reduce contagion rates and the need to reopen their economies. The experience hitherto lived has provided data on the evolution of the pandemic, in particular the population dynamics as a result of the public health measures enacted. This allows the formulation of forecasting mathematical models to anticipate the consequences of political decisions. Here we propose a model to do so and apply it to the case of Portugal. With a mathematical deterministic model, described by a system of ordinary differential equations, we fit the real evolution of COVID-19 in this country. After identification of the population readiness to follow social restrictions, by analyzing the social media, we incorporate this effect in a version of the model that allow us to check different scenarios. This is realized by considering a Monte Carlo discrete version of the previous model coupled via a complex network. Then, we apply optimal control theory to maximize the number of people returning to “normal life” and minimizing the number of active infected individuals with minimal economical costs while warranting a low level of hospitalizations. This work allows testing various scenarios of pandemic management (closure of sectors of the economy, partial/total compliance with protection measures by citizens, number of beds in intensive care units, etc.), ensuring the responsiveness of the health system, thus being a public health decision support tool.

Impact of a New SARS-CoV-2 Variant on the Population: A Mathematical Modeling Approach

“…We include the asymptomatic carriers of the SARS-CoV-2, who are nevertheless able to spread the virus. The reason to include them is that asymptomatic people are very important contributors in the spread of the SARS-CoV-2, and this subpopulation has been considered in other studies related to the COVID-19 pandemic [20,72,[76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92]. It has been found that quantitative SARS-CoV-2 viral loads were similarly high for infected individuals with symptoms, pre-symptomatic, or asymptomatic.…”

One Year of the COVID-19 Pandemic in Galicia: A Global View of Age-Group Statistics during Three Waves

“…Since the beginning of the pandemic of COVID-19, a number of attempts to predict its evolution have been published, helping the health authorities with their strategies. The mathematical tools used vary from compartmental models [1][2][3][4][5], including fractional derivatives [6,7], to statistical tools [8][9][10]. With the aim of helping health authorities to avoid hospital resource collapses, some works have been used, e.g., to predict the numbers of required beds at intensive care units [11].…”

Mathematical Epidemiological Model of the Propagation and Prevention of Infectious Diseases Considering COVID-19 Vaccination

“…Since the beginning of 2020, numerous studies have been published on COVID-19 and SARS-CoV-2 in fields ranging from microscopic to macroscopic, including molecular biology, medical and clinical studies, epidemiology, and political decision making, with each field applying its characteristic methodologies. As examples of reports with similar focus and methodology to this study, research on COVID-19 modelling has considered countries or regions such as France [22], Italy [23], the Basque country [24], Spain [25], Portugal [26], Hong Kong [27], China [28,29,30], and the UK [31]. These studies discussed recurrence and rebound of the epidemics, effects of protection measures and lockdown, widespread testing, contact tracing, care capacities, or scenarios of pandemic management.…”