Correlations between web searches and covid-19 epidemiological indicators in Brazil
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFMG |
Texto Completo: | http://hdl.handle.net/1843/68191 https://orcid.org/0000-0002-0893-1446 https://orcid.org/0000-0002-6533-5229 https://orcid.org/0000-0002-1150-2546 https://orcid.org/0000-0003-1821-3912 https://orcid.org/0000-0002-8176-586X https://orcid.org/0000-0002-1979-1522 https://orcid.org/0000-0002-6534-5785 https://orcid.org/0000-0001-8600-4077 https://orcid.org/0000-0002-1654-5852 https://orcid.org/0000-0002-9542-090X https://orcid.org/0000-0002-0139-107X https://orcid.org/0000-0002-5302-2946 https://orcid.org/0000-0001-6125-642X https://orcid.org/0000-0001-5951-3562 https://orcid.org/0000-0002-2614-2723 https://orcid.org/0000-0003-0590-4538 https://orcid.org/0000-0002-2653-9835 https://orcid.org/0000-0001-7199-3888 https://orcid.org/0000-0001-9599-0198 https://orcid.org/0000-0001-7662-6737 https://orcid.org/0000-0003-0842-4732 https://orcid.org/0000-0001-6784-0002 |
Resumo: | COVID-19 rapidly spread across the world in an unprecedented outbreak with a massive number of infected and fatalities. The pandemic was heavily discussed and searched on the internet, which generated big amounts of data related to it. This led to the possibility of attempting to forecast coronavirus indicators using the internet data. For this study, Google Trends statistics for 124 selected search terms related to pandemic were used in an attempt to find which keywords had the best Spearman correlations with a lag, as well as a forecasting model. It was found that keywords related to coronavirus testing among some others, such as “I have contracted covid”, had high correlations (≥0.7) with few weeks of lag (≤4 weeks). Besides that, the ARIMAX model using those keywords had promising results in predicting the increase or decrease of epidemiological indicators, although it was not able to predict their exact values. Thus, we found that Google Trends data may be useful for predicting outbreaks of coronavirus a few weeks before they happen, and may be used as an auxiliary tool in monitoring and forecasting the disease in Brazil. |
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2024-05-10T20:54:04Z2024-05-10T20:54:04Z20226511010.1590/1678-4324-20222106481678-4324http://hdl.handle.net/1843/68191https://orcid.org/0000-0002-0893-1446https://orcid.org/0000-0002-6533-5229https://orcid.org/0000-0002-1150-2546https://orcid.org/0000-0003-1821-3912https://orcid.org/0000-0002-8176-586Xhttps://orcid.org/0000-0002-1979-1522https://orcid.org/0000-0002-6534-5785https://orcid.org/0000-0001-8600-4077https://orcid.org/0000-0002-1654-5852https://orcid.org/0000-0002-9542-090Xhttps://orcid.org/0000-0002-0139-107Xhttps://orcid.org/0000-0002-5302-2946https://orcid.org/0000-0001-6125-642Xhttps://orcid.org/0000-0001-5951-3562https://orcid.org/0000-0002-2614-2723https://orcid.org/0000-0003-0590-4538https://orcid.org/0000-0002-2653-9835https://orcid.org/0000-0001-7199-3888https://orcid.org/0000-0001-9599-0198https://orcid.org/0000-0001-7662-6737https://orcid.org/0000-0003-0842-4732https://orcid.org/0000-0001-6784-0002COVID-19 rapidly spread across the world in an unprecedented outbreak with a massive number of infected and fatalities. The pandemic was heavily discussed and searched on the internet, which generated big amounts of data related to it. This led to the possibility of attempting to forecast coronavirus indicators using the internet data. For this study, Google Trends statistics for 124 selected search terms related to pandemic were used in an attempt to find which keywords had the best Spearman correlations with a lag, as well as a forecasting model. It was found that keywords related to coronavirus testing among some others, such as “I have contracted covid”, had high correlations (≥0.7) with few weeks of lag (≤4 weeks). Besides that, the ARIMAX model using those keywords had promising results in predicting the increase or decrease of epidemiological indicators, although it was not able to predict their exact values. Thus, we found that Google Trends data may be useful for predicting outbreaks of coronavirus a few weeks before they happen, and may be used as an auxiliary tool in monitoring and forecasting the disease in Brazil.engUniversidade Federal de Minas GeraisUFMGBrasilFALE - FACULDADE DE LETRASBrazilian Archives of Biology and TechnologySaúde - IndicadoresGoogle TrendsInfodemiologyEpidemiological predictionsDigital healthCorrelations between web searches and covid-19 epidemiological indicators in Brazilinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://www.scielo.br/j/babt/a/FN9Rwk4DpGDCRmPg8V9tkgL/?lang=endoi:10.1590/1678-4324-2022210648Marcelo Sartori LocatelliAnne Isabelle Rodrigues de CarvalhoLeandro M. V. SouzaGabriela P. F. PaixãoElisa França ChavesGuilherme Bezerra Dos SantosRafael Vinícius dos SantosAmanda Cupertino de FreitasMatheus G. FloresRachel F. BiezunerRodolfo Lins CardosoEvandro Landulfo Teixeira Paradela CunhaRodrigo Machado FonsecaAna Paula Couto da SilvaWagner Meira JrJanaína GuiginskiRamon A. S. FrancoTereza BernardesPedro Loures AlzamoraDaniel Victor F. da SilvaMarcelo Augusto s GanemThiago H. M. Santosinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/68191/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALCorrelations between Web Searches and COVID-19 Epidemiological Indicators in Brazil.pdfCorrelations between Web Searches and COVID-19 Epidemiological Indicators in Brazil.pdfapplication/pdf871104https://repositorio.ufmg.br/bitstream/1843/68191/2/Correlations%20between%20Web%20Searches%20and%20COVID-19%20Epidemiological%20Indicators%20in%20Brazil.pdfb13c39401fb2cbcb5bd77f32ea0a55ffMD521843/681912024-05-10 17:54:04.728oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2024-05-10T20:54:04Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false |
dc.title.pt_BR.fl_str_mv |
Correlations between web searches and covid-19 epidemiological indicators in Brazil |
title |
Correlations between web searches and covid-19 epidemiological indicators in Brazil |
spellingShingle |
Correlations between web searches and covid-19 epidemiological indicators in Brazil Marcelo Sartori Locatelli Google Trends Infodemiology Epidemiological predictions Digital health Saúde - Indicadores |
title_short |
Correlations between web searches and covid-19 epidemiological indicators in Brazil |
title_full |
Correlations between web searches and covid-19 epidemiological indicators in Brazil |
title_fullStr |
Correlations between web searches and covid-19 epidemiological indicators in Brazil |
title_full_unstemmed |
Correlations between web searches and covid-19 epidemiological indicators in Brazil |
title_sort |
Correlations between web searches and covid-19 epidemiological indicators in Brazil |
author |
Marcelo Sartori Locatelli |
author_facet |
Marcelo Sartori Locatelli Anne Isabelle Rodrigues de Carvalho Leandro M. V. Souza Gabriela P. F. Paixão Elisa França Chaves Guilherme Bezerra Dos Santos Rafael Vinícius dos Santos Amanda Cupertino de Freitas Matheus G. Flores Rachel F. Biezuner Rodolfo Lins Cardoso Evandro Landulfo Teixeira Paradela Cunha Rodrigo Machado Fonseca Ana Paula Couto da Silva Wagner Meira Jr Janaína Guiginski Ramon A. S. Franco Tereza Bernardes Pedro Loures Alzamora Daniel Victor F. da Silva Marcelo Augusto s Ganem Thiago H. M. Santos |
author_role |
author |
author2 |
Anne Isabelle Rodrigues de Carvalho Leandro M. V. Souza Gabriela P. F. Paixão Elisa França Chaves Guilherme Bezerra Dos Santos Rafael Vinícius dos Santos Amanda Cupertino de Freitas Matheus G. Flores Rachel F. Biezuner Rodolfo Lins Cardoso Evandro Landulfo Teixeira Paradela Cunha Rodrigo Machado Fonseca Ana Paula Couto da Silva Wagner Meira Jr Janaína Guiginski Ramon A. S. Franco Tereza Bernardes Pedro Loures Alzamora Daniel Victor F. da Silva Marcelo Augusto s Ganem Thiago H. M. Santos |
author2_role |
author author author author author author author author author author author author author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Marcelo Sartori Locatelli Anne Isabelle Rodrigues de Carvalho Leandro M. V. Souza Gabriela P. F. Paixão Elisa França Chaves Guilherme Bezerra Dos Santos Rafael Vinícius dos Santos Amanda Cupertino de Freitas Matheus G. Flores Rachel F. Biezuner Rodolfo Lins Cardoso Evandro Landulfo Teixeira Paradela Cunha Rodrigo Machado Fonseca Ana Paula Couto da Silva Wagner Meira Jr Janaína Guiginski Ramon A. S. Franco Tereza Bernardes Pedro Loures Alzamora Daniel Victor F. da Silva Marcelo Augusto s Ganem Thiago H. M. Santos |
dc.subject.por.fl_str_mv |
Google Trends Infodemiology Epidemiological predictions Digital health |
topic |
Google Trends Infodemiology Epidemiological predictions Digital health Saúde - Indicadores |
dc.subject.other.pt_BR.fl_str_mv |
Saúde - Indicadores |
description |
COVID-19 rapidly spread across the world in an unprecedented outbreak with a massive number of infected and fatalities. The pandemic was heavily discussed and searched on the internet, which generated big amounts of data related to it. This led to the possibility of attempting to forecast coronavirus indicators using the internet data. For this study, Google Trends statistics for 124 selected search terms related to pandemic were used in an attempt to find which keywords had the best Spearman correlations with a lag, as well as a forecasting model. It was found that keywords related to coronavirus testing among some others, such as “I have contracted covid”, had high correlations (≥0.7) with few weeks of lag (≤4 weeks). Besides that, the ARIMAX model using those keywords had promising results in predicting the increase or decrease of epidemiological indicators, although it was not able to predict their exact values. Thus, we found that Google Trends data may be useful for predicting outbreaks of coronavirus a few weeks before they happen, and may be used as an auxiliary tool in monitoring and forecasting the disease in Brazil. |
publishDate |
2022 |
dc.date.issued.fl_str_mv |
2022 |
dc.date.accessioned.fl_str_mv |
2024-05-10T20:54:04Z |
dc.date.available.fl_str_mv |
2024-05-10T20:54:04Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
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article |
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publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1843/68191 |
dc.identifier.doi.pt_BR.fl_str_mv |
10.1590/1678-4324-2022210648 |
dc.identifier.issn.pt_BR.fl_str_mv |
1678-4324 |
dc.identifier.orcid.pt_BR.fl_str_mv |
https://orcid.org/0000-0002-0893-1446 https://orcid.org/0000-0002-6533-5229 https://orcid.org/0000-0002-1150-2546 https://orcid.org/0000-0003-1821-3912 https://orcid.org/0000-0002-8176-586X https://orcid.org/0000-0002-1979-1522 https://orcid.org/0000-0002-6534-5785 https://orcid.org/0000-0001-8600-4077 https://orcid.org/0000-0002-1654-5852 https://orcid.org/0000-0002-9542-090X https://orcid.org/0000-0002-0139-107X https://orcid.org/0000-0002-5302-2946 https://orcid.org/0000-0001-6125-642X https://orcid.org/0000-0001-5951-3562 https://orcid.org/0000-0002-2614-2723 https://orcid.org/0000-0003-0590-4538 https://orcid.org/0000-0002-2653-9835 https://orcid.org/0000-0001-7199-3888 https://orcid.org/0000-0001-9599-0198 https://orcid.org/0000-0001-7662-6737 https://orcid.org/0000-0003-0842-4732 https://orcid.org/0000-0001-6784-0002 |
identifier_str_mv |
10.1590/1678-4324-2022210648 1678-4324 |
url |
http://hdl.handle.net/1843/68191 https://orcid.org/0000-0002-0893-1446 https://orcid.org/0000-0002-6533-5229 https://orcid.org/0000-0002-1150-2546 https://orcid.org/0000-0003-1821-3912 https://orcid.org/0000-0002-8176-586X https://orcid.org/0000-0002-1979-1522 https://orcid.org/0000-0002-6534-5785 https://orcid.org/0000-0001-8600-4077 https://orcid.org/0000-0002-1654-5852 https://orcid.org/0000-0002-9542-090X https://orcid.org/0000-0002-0139-107X https://orcid.org/0000-0002-5302-2946 https://orcid.org/0000-0001-6125-642X https://orcid.org/0000-0001-5951-3562 https://orcid.org/0000-0002-2614-2723 https://orcid.org/0000-0003-0590-4538 https://orcid.org/0000-0002-2653-9835 https://orcid.org/0000-0001-7199-3888 https://orcid.org/0000-0001-9599-0198 https://orcid.org/0000-0001-7662-6737 https://orcid.org/0000-0003-0842-4732 https://orcid.org/0000-0001-6784-0002 |
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eng |
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eng |
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Brazilian Archives of Biology and Technology |
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openAccess |
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Universidade Federal de Minas Gerais |
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UFMG |
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Brasil |
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FALE - FACULDADE DE LETRAS |
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Universidade Federal de Minas Gerais |
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