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Modeling epidemic growth curves using nonlinear rational polynomial equations: an application to Brazil's COVID-19 data

This paper reports a broad study using epidemic-related counting data of COVID-19 disease caused by the novel coronavirus (SARS-CoV-2). The considered dataset refers to Brazil's daily and accumulated counts of reported cases and deaths in a fixed period (from January 22 to June 16, 2020). For t...

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Principais autores: De Oliveira, Ricardo Puziol, Achcar, Jorge Alberto, Bertoli, Wesley, Mazucheli, Josmar, Miranda, Yara Campos
Formato: Artigo
Idioma: Inglês
Publicado em: Universidade Tecnológica Federal do Paraná (UTFPR) 2022
Acesso em linha: http://periodicos.utfpr.edu.br/rts/article/view/13534
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spelling peri-article-135342024-05-01T15:48:40Z Modeling epidemic growth curves using nonlinear rational polynomial equations: an application to Brazil's COVID-19 data De Oliveira, Ricardo Puziol Achcar, Jorge Alberto Bertoli, Wesley Mazucheli, Josmar Miranda, Yara Campos 1.02.03.00-1 COVID-19 counting data; Gaussian errors; Nonlinear models; Rational polynomial functions; SARS-CoV-2 This paper reports a broad study using epidemic-related counting data of COVID-19 disease caused by the novel coronavirus (SARS-CoV-2). The considered dataset refers to Brazil's daily and accumulated counts of reported cases and deaths in a fixed period (from January 22 to June 16, 2020). For the data analysis, it has been adopted a nonlinear rational polynomial function to model the mentioned counts assuming Gaussian errors. The least-squares method was applied to fit the proposed model. We have noticed that the curves are still increasing after June 16, with no evidence of peak being reached or decreasing behavior in the period for new reported cases and confirmed deaths by the disease. The obtained results are consistent and highlight the adopted model's capability to accurately predict the behavior of Brazil's COVID-19 growth curve in the observed time-frame. Universidade Tecnológica Federal do Paraná (UTFPR) Jorge A. Achcar CNPq(301923/2019-1) Josmar Mazucheli (064/2019 - UEM/Fundação Araucária). 2022-01-02 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf http://periodicos.utfpr.edu.br/rts/article/view/13534 10.3895/rts.v18n50.13534 Revista Tecnologia e Sociedade; v. 18, n. 50 (2022); 35-47 Revista Tecnologia e Sociedade; v. 18, n. 50 (2022); 35-47 1984-3526 1809-0044 10.3895/rts.v18n50 eng http://periodicos.utfpr.edu.br/rts/article/view/13534/8620 Direitos autorais 2021 CC-BY http://creativecommons.org/licenses/by/4.0
institution Universidade Tecnológica Federal do Paraná
collection PERI
language Inglês
format Artigo
author De Oliveira, Ricardo Puziol
Achcar, Jorge Alberto
Bertoli, Wesley
Mazucheli, Josmar
Miranda, Yara Campos
spellingShingle De Oliveira, Ricardo Puziol
Achcar, Jorge Alberto
Bertoli, Wesley
Mazucheli, Josmar
Miranda, Yara Campos
Modeling epidemic growth curves using nonlinear rational polynomial equations: an application to Brazil's COVID-19 data
author_sort De Oliveira, Ricardo Puziol
title Modeling epidemic growth curves using nonlinear rational polynomial equations: an application to Brazil's COVID-19 data
title_short Modeling epidemic growth curves using nonlinear rational polynomial equations: an application to Brazil's COVID-19 data
title_full Modeling epidemic growth curves using nonlinear rational polynomial equations: an application to Brazil's COVID-19 data
title_fullStr Modeling epidemic growth curves using nonlinear rational polynomial equations: an application to Brazil's COVID-19 data
title_full_unstemmed Modeling epidemic growth curves using nonlinear rational polynomial equations: an application to Brazil's COVID-19 data
title_sort modeling epidemic growth curves using nonlinear rational polynomial equations: an application to brazil's covid-19 data
description This paper reports a broad study using epidemic-related counting data of COVID-19 disease caused by the novel coronavirus (SARS-CoV-2). The considered dataset refers to Brazil's daily and accumulated counts of reported cases and deaths in a fixed period (from January 22 to June 16, 2020). For the data analysis, it has been adopted a nonlinear rational polynomial function to model the mentioned counts assuming Gaussian errors. The least-squares method was applied to fit the proposed model. We have noticed that the curves are still increasing after June 16, with no evidence of peak being reached or decreasing behavior in the period for new reported cases and confirmed deaths by the disease. The obtained results are consistent and highlight the adopted model's capability to accurately predict the behavior of Brazil's COVID-19 growth curve in the observed time-frame.
publisher Universidade Tecnológica Federal do Paraná (UTFPR)
publishDate 2022
url http://periodicos.utfpr.edu.br/rts/article/view/13534
_version_ 1805535496395292672
score 10,814766

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