A control approach to the Covid-19 disease using a SEIHRD dynamical model

Abstract

The recent worldwide epidemic of Covid-19 disease, for which there is no vaccine or medications to prevent or cure it, led to the adoption of public health measures by governments and populations in most of the affected countries to avoid the contagion and its spread. These measures are known as nonpharmaceutical interventions (NPIs) and their implementation clearly produces social unrest as well as greatly affects the economy. Frequently, NPIs are implemented with an intensity quantified in an ad hoc manner. Control theory offers a worthwhile tool for determining the optimal intensity of the NPIs in order to avoid the collapse of the healthcare system while keeping them as low as possible, yielding in a policymakers concrete guidance. We propose here the use of a simple proportional controller that is robust to large parametric uncertainties in the model used.

Potentials of constrained sliding mode control as an intervention guide to manage COVID19 spread

“…a lockdown, changes in public transportation rules) require a few days of publishing, there are other actions that can be readily implemented (e.g. number personal transit passes approved per day) [11].…”

“…In [10], age structure is considered for the design of quarantine rates of individuals. The application of a proportional controller is proposed in [11] in which the objective is to maintain the number of hospitalized individuals below a set-point. In [12], a dynamic optimization approach is utilized with the aim of mini-mizing a socioeconomic cost function subject to limiting peak value of infections.…”

A Review of Matrix SIR Arino Epidemic Models

“…We begin by recalling in Section 2 several basic explicit formulas for the SIR model. Section 3 presents the corresponding SIR-PH generalizations, and Section 4 offers some applications: the SEIHRD model [23][24][25][26][27], which adds to the classic SEIR (susceptible + exposed + infectious + recovered) a hospitalized (H) class and a dead class (D); the SEICHRD model [28] which adds a critically ill class (C); the SEIARD [29] and SEIAHR/SEIRAH(D) models [30][31][32][33][34][35][36], which add an asymptomatic class (A); and the SI 3 QR model [37]. This is just a sample chosen from some of our favorite COVID papers.…”

Controle Epidemiologico da COVID-19 atraves da Teoria de Sistemas a Estrutura Variavel e Modos Deslizantes

“…Até o presente momento, não existe vacina contra esse vírus ou medicamentos eficazes para a cura. Por sua vez, os sistemas de saúde ficam cada vez mais sobrecarregados, na tentativa de mitigar as consequências, evitando complicações e resultados ainda mais graves (Pazos, 2020). Embora medidas como quarentena e isolamento social sejam possibilidades de controle em situações de pandemia, o seu prolongamento pode ser prejudicialà economia do país.…”