Naive statistical analyses for COVID-19: application to data from Brazil and Italy
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Outros Autores: | , |
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. |
id |
UFLA_32dc563c85664d42d4b40093ff7426cd |
---|---|
oai_identifier_str |
oai:localhost:1/50659 |
network_acronym_str |
UFLA |
network_name_str |
Repositório Institucional da UFLA |
repository_id_str |
|
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 |
_version_ |
1815439268160471040 |