A Class of Exponential-Type State Observer Design for the Estimation of the Risk Population on the COVID-19 Pandemic

Abstract

The aim of this study was to present a class of exponential-type state observer designsfor the estimation of the risk population on the COVID-19 pandemic. Theobserver is based on the SIDUR-type model for the prediction of the risk populationin the COVID-19 pandemic, which is one of the popular employed mathematicalmodels. The proposed observer considers as measured variables, the number of diagnosedpersons who are infected and detected by test and the number of removedpersons who either recover or die after being diagnosed notice that these consideredmeasured signals are commonly reported by the public health systems. A local observabilityanalysis shows the observability characteristics of the considered model.The most important feature of this observation method is the convergence can estimatethe number of infected persons who are undetected by the public healthsystems. Finally, in silico experiments show the satisfactory performance of the proposedmethodology and asymptotic stability of the estimation convergence is shownin a Lyapunov framework.The utility of this methodology is that it provides informationto support public health decision making during a pandemic, and a strengthof the work is that it can as a reference for a new epidemics spread.