Naive statistical analyses for COVID-19: application to data from Brazil and Italy

Detalhes bibliográficos
Autor(a) principal: Pereira, Carlos Alberto Bragança
Data de Publicação: 2022
Outros Autores: Nakamura, Luiz Ricardo, Rodrigues, Paulo Canas
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/50659
Resumo: This article is a direct consequence of the authors' desire to discuss the role of statistics in data analysis. The analysis of coronavirus (COVID-19) databases are used as to show simple, but powerful statistical frameworks. We do believe that models for assessing future trends in temporal data in general, and in cases and/or deaths of COVID-19, belongs to the area of (Bio)Statistics. Just as engineers use knowledge of physics, chemistry and often architecture, when constructing bridges, buildings and roads, statisticians use knowledge of mathematics, computer science and even physics for modelling, analysing, and forecasting in order to transform data into information. While the statistician's contribution is rarely acknowledged, everyone knows that a building is a work of an engineer. Nonetheless, nowadays statistics has been gaining the attention that it deserves due to the rise of big data and data science that was built on the foundations of statistics. This article shows that, even with only basic knowledge of statistics, one can adequately collaborate with the community in dealing with very important issues such as the COVID-19 numbers. In order to model and to obtain predictions we use well-known distributions to statisticians working on survival analysis: gamma, Weibull and log-normal distributions. We also make use of singular spectrum analysis, a simple non-parametric time series methodology, for an analogous purpose. Survival analysis is a research area widely used in Biostatistics and even in Reliability, while time series analysis is widely used across areas where the data is measured along the time.
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spelling Naive statistical analyses for COVID-19: application to data from Brazil and ItalyCOVID-19Statistics in practiceSurvival analysisTime series analysisEstatísticas na práticaAnálise de sobrevivênciaAnálise de séries temporaisThis article is a direct consequence of the authors' desire to discuss the role of statistics in data analysis. The analysis of coronavirus (COVID-19) databases are used as to show simple, but powerful statistical frameworks. We do believe that models for assessing future trends in temporal data in general, and in cases and/or deaths of COVID-19, belongs to the area of (Bio)Statistics. Just as engineers use knowledge of physics, chemistry and often architecture, when constructing bridges, buildings and roads, statisticians use knowledge of mathematics, computer science and even physics for modelling, analysing, and forecasting in order to transform data into information. While the statistician's contribution is rarely acknowledged, everyone knows that a building is a work of an engineer. Nonetheless, nowadays statistics has been gaining the attention that it deserves due to the rise of big data and data science that was built on the foundations of statistics. This article shows that, even with only basic knowledge of statistics, one can adequately collaborate with the community in dealing with very important issues such as the COVID-19 numbers. In order to model and to obtain predictions we use well-known distributions to statisticians working on survival analysis: gamma, Weibull and log-normal distributions. We also make use of singular spectrum analysis, a simple non-parametric time series methodology, for an analogous purpose. Survival analysis is a research area widely used in Biostatistics and even in Reliability, while time series analysis is widely used across areas where the data is measured along the time.Universidade Federal de Lavras2022-07-20T18:48:04Z2022-07-20T18:48:04Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfPEREIRA, C. A. B.; NAKAMURA, L. R.; RODRIGUES, P. C. Naive statistical analyses for COVID-19: application to data from Brazil and Italy. Brazilian Journal of Biometrics, Lavras, v. 39, n. 1, p. 158-176, 2021. DOI: 10.28951/rbb.v39i1.515.http://repositorio.ufla.br/jspui/handle/1/50659Brazilian Journal of Biometricsreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessPereira, Carlos Alberto BragançaNakamura, Luiz RicardoRodrigues, Paulo Canaseng2023-05-19T18:51:08Zoai:localhost:1/50659Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-19T18:51:08Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Naive statistical analyses for COVID-19: application to data from Brazil and Italy
title Naive statistical analyses for COVID-19: application to data from Brazil and Italy
spellingShingle Naive statistical analyses for COVID-19: application to data from Brazil and Italy
Pereira, Carlos Alberto Bragança
COVID-19
Statistics in practice
Survival analysis
Time series analysis
Estatísticas na prática
Análise de sobrevivência
Análise de séries temporais
title_short Naive statistical analyses for COVID-19: application to data from Brazil and Italy
title_full Naive statistical analyses for COVID-19: application to data from Brazil and Italy
title_fullStr Naive statistical analyses for COVID-19: application to data from Brazil and Italy
title_full_unstemmed Naive statistical analyses for COVID-19: application to data from Brazil and Italy
title_sort Naive statistical analyses for COVID-19: application to data from Brazil and Italy
author Pereira, Carlos Alberto Bragança
author_facet Pereira, Carlos Alberto Bragança
Nakamura, Luiz Ricardo
Rodrigues, Paulo Canas
author_role author
author2 Nakamura, Luiz Ricardo
Rodrigues, Paulo Canas
author2_role author
author
dc.contributor.author.fl_str_mv Pereira, Carlos Alberto Bragança
Nakamura, Luiz Ricardo
Rodrigues, Paulo Canas
dc.subject.por.fl_str_mv COVID-19
Statistics in practice
Survival analysis
Time series analysis
Estatísticas na prática
Análise de sobrevivência
Análise de séries temporais
topic COVID-19
Statistics in practice
Survival analysis
Time series analysis
Estatísticas na prática
Análise de sobrevivência
Análise de séries temporais
description This article is a direct consequence of the authors' desire to discuss the role of statistics in data analysis. The analysis of coronavirus (COVID-19) databases are used as to show simple, but powerful statistical frameworks. We do believe that models for assessing future trends in temporal data in general, and in cases and/or deaths of COVID-19, belongs to the area of (Bio)Statistics. Just as engineers use knowledge of physics, chemistry and often architecture, when constructing bridges, buildings and roads, statisticians use knowledge of mathematics, computer science and even physics for modelling, analysing, and forecasting in order to transform data into information. While the statistician's contribution is rarely acknowledged, everyone knows that a building is a work of an engineer. Nonetheless, nowadays statistics has been gaining the attention that it deserves due to the rise of big data and data science that was built on the foundations of statistics. This article shows that, even with only basic knowledge of statistics, one can adequately collaborate with the community in dealing with very important issues such as the COVID-19 numbers. In order to model and to obtain predictions we use well-known distributions to statisticians working on survival analysis: gamma, Weibull and log-normal distributions. We also make use of singular spectrum analysis, a simple non-parametric time series methodology, for an analogous purpose. Survival analysis is a research area widely used in Biostatistics and even in Reliability, while time series analysis is widely used across areas where the data is measured along the time.
publishDate 2022
dc.date.none.fl_str_mv 2022-07-20T18:48:04Z
2022-07-20T18:48:04Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv PEREIRA, C. A. B.; NAKAMURA, L. R.; RODRIGUES, P. C. Naive statistical analyses for COVID-19: application to data from Brazil and Italy. Brazilian Journal of Biometrics, Lavras, v. 39, n. 1, p. 158-176, 2021. DOI: 10.28951/rbb.v39i1.515.
http://repositorio.ufla.br/jspui/handle/1/50659
identifier_str_mv PEREIRA, C. A. B.; NAKAMURA, L. R.; RODRIGUES, P. C. Naive statistical analyses for COVID-19: application to data from Brazil and Italy. Brazilian Journal of Biometrics, Lavras, v. 39, n. 1, p. 158-176, 2021. DOI: 10.28951/rbb.v39i1.515.
url http://repositorio.ufla.br/jspui/handle/1/50659
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
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 Federal de Lavras
publisher.none.fl_str_mv Universidade Federal de Lavras
dc.source.none.fl_str_mv Brazilian Journal of Biometrics
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
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