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An algorithm for the robust estimation of the COVID-19 pandemic's population by considering undetected individuals.
Martínez-Guerra, Rafael; Flores-Flores, Juan Pablo.
  • Martínez-Guerra R; Departamento de Control Automático, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional. Av Instituto Politécnico Nacional 2508, San Pedro Zacatenco, Gustavo A. Madero, Mexico City 07360, Mexico.
  • Flores-Flores JP; Departamento de Control Automático, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional. Av Instituto Politécnico Nacional 2508, San Pedro Zacatenco, Gustavo A. Madero, Mexico City 07360, Mexico.
Appl Math Comput ; 405: 126273, 2021 Sep 15.
Article in English | MEDLINE | ID: covidwho-1174069
Semantic information from SemMedBD (by NLM)
1. Infected PROCESS_OF Persons
Subject
Infected
Predicate
PROCESS_OF
Object
Persons
2. Infected PROCESS_OF Persons
Subject
Infected
Predicate
PROCESS_OF
Object
Persons
ABSTRACT
Due to the current COVID-19 pandemic, much effort has been put on studying the spread of infectious diseases to propose more adequate health politics. The most effective surveillance system consists of doing massive tests. Nonetheless, many countries cannot afford this class of health campaigns due to limited resources. Thus, a transmission model is a viable alternative to study the dynamics of the pandemic. The most used are the Susceptible, Infected and Removed type models (SIR). In this study, we tackle the population estimation problem of the A-SIR model, which takes into account asymptomatic or undetected individuals. By means of an algebraic differential approach, we design a model-free (no copy system) reduced-order estimation algorithm (observer) to determine the different non-measured population groups. We study two types of estimation algorithms Proportional and Proportional-Integral. Both shown fast convergence speed, as well as a minimal estimation error. Additionally, we introduce random fluctuations in our analysis to represent changes in the external conditions and which result in poor measurements. The numerical results reveal that both model-free estimators are robust despite the presence of these fluctuations. As a point of reference, we apply the classical Luenberger type observer to our estimation problem and compare the results. Finally, we consider real data of infected individuals in Mexico City, reported from February 2020 to March 2021, and estimate the non-measured populations. Our work's main goal is to proportionate a simple and therefore, an accessible methodology to estimate the behavior of the COVID-19 pandemic from the available data, such that the competent authorities can propose more adequate health politics.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Randomized controlled trials Language: English Journal: Appl Math Comput Year: 2021 Document Type: Article Affiliation country: J.amc.2021.126273

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Randomized controlled trials Language: English Journal: Appl Math Comput Year: 2021 Document Type: Article Affiliation country: J.amc.2021.126273