Modeling epidemic growth curves using nonlinear rational polynomial equations: an application to Brazil's COVID-19 data
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Tecnologia e Sociedade (Online) |
Texto Completo: | https://periodicos.utfpr.edu.br/rts/article/view/13534 |
Resumo: | 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. |
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Revista Tecnologia e Sociedade (Online) |
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Modeling epidemic growth curves using nonlinear rational polynomial equations: an application to Brazil's COVID-19 data1.02.03.00-1COVID-19 counting data; Gaussian errors; Nonlinear models; Rational polynomial functions; SARS-CoV-2This 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).De Oliveira, Ricardo PuziolAchcar, Jorge AlbertoBertoli, WesleyMazucheli, JosmarMiranda, Yara Campos2022-01-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.utfpr.edu.br/rts/article/view/1353410.3895/rts.v18n50.13534Revista Tecnologia e Sociedade; v. 18, n. 50 (2022); 35-47Revista Tecnologia e Sociedade; v. 18, n. 50 (2022); 35-471984-35261809-004410.3895/rts.v18n50reponame:Revista Tecnologia e Sociedade (Online)instname:Universidade Tecnológica Federal do Paraná (UTFPR)instacron:UTFPRenghttps://periodicos.utfpr.edu.br/rts/article/view/13534/8620Direitos autorais 2021 CC-BYhttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess2024-05-01T15:48:40Zoai:periodicos.utfpr:article/13534Revistahttps://periodicos.ifrs.edu.br/index.php/tearPUBhttps://periodicos.utfpr.edu.br/rts/oai||rts-ct@utfpr.edu.br1984-35261809-0044opendoar:2024-05-01T15:48:40Revista Tecnologia e Sociedade (Online) - Universidade Tecnológica Federal do Paraná (UTFPR)false |
dc.title.none.fl_str_mv |
Modeling epidemic growth curves using nonlinear rational polynomial equations: an application to Brazil's COVID-19 data |
title |
Modeling epidemic growth curves using nonlinear rational polynomial equations: an application to Brazil's COVID-19 data |
spellingShingle |
Modeling epidemic growth curves using nonlinear rational polynomial equations: an application to Brazil's COVID-19 data De Oliveira, Ricardo Puziol 1.02.03.00-1 COVID-19 counting data; Gaussian errors; Nonlinear models; Rational polynomial functions; SARS-CoV-2 |
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 |
author |
De Oliveira, Ricardo Puziol |
author_facet |
De Oliveira, Ricardo Puziol Achcar, Jorge Alberto Bertoli, Wesley Mazucheli, Josmar Miranda, Yara Campos |
author_role |
author |
author2 |
Achcar, Jorge Alberto Bertoli, Wesley Mazucheli, Josmar Miranda, Yara Campos |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Jorge A. Achcar CNPq(301923/2019-1) Josmar Mazucheli (064/2019 - UEM/Fundação Araucária). |
dc.contributor.author.fl_str_mv |
De Oliveira, Ricardo Puziol Achcar, Jorge Alberto Bertoli, Wesley Mazucheli, Josmar Miranda, Yara Campos |
dc.subject.por.fl_str_mv |
1.02.03.00-1 COVID-19 counting data; Gaussian errors; Nonlinear models; Rational polynomial functions; SARS-CoV-2 |
topic |
1.02.03.00-1 COVID-19 counting data; Gaussian errors; Nonlinear models; Rational polynomial functions; SARS-CoV-2 |
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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-02 |
dc.type.none.fl_str_mv |
|
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.utfpr.edu.br/rts/article/view/13534 10.3895/rts.v18n50.13534 |
url |
https://periodicos.utfpr.edu.br/rts/article/view/13534 |
identifier_str_mv |
10.3895/rts.v18n50.13534 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.utfpr.edu.br/rts/article/view/13534/8620 |
dc.rights.driver.fl_str_mv |
Direitos autorais 2021 CC-BY http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Direitos autorais 2021 CC-BY http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Tecnológica Federal do Paraná (UTFPR) |
publisher.none.fl_str_mv |
Universidade Tecnológica Federal do Paraná (UTFPR) |
dc.source.none.fl_str_mv |
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 reponame:Revista Tecnologia e Sociedade (Online) instname:Universidade Tecnológica Federal do Paraná (UTFPR) instacron:UTFPR |
instname_str |
Universidade Tecnológica Federal do Paraná (UTFPR) |
instacron_str |
UTFPR |
institution |
UTFPR |
reponame_str |
Revista Tecnologia e Sociedade (Online) |
collection |
Revista Tecnologia e Sociedade (Online) |
repository.name.fl_str_mv |
Revista Tecnologia e Sociedade (Online) - Universidade Tecnológica Federal do Paraná (UTFPR) |
repository.mail.fl_str_mv |
||rts-ct@utfpr.edu.br |
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1800237663031132160 |