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: | Brazilian Archives of Biology and Technology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100314 |
Resumo: | Abstract: 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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Brazilian Archives of Biology and Technology |
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Correlations between Web Searches and COVID-19 Epidemiological Indicators in BrazilGoogle Trendsinfodemiologyepidemiological predictionsdigital healthAbstract: 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.Instituto de Tecnologia do Paraná - Tecpar2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100314Brazilian Archives of Biology and Technology v.65 2022reponame:Brazilian Archives of Biology and Technologyinstname:Instituto de Tecnologia do Paraná (Tecpar)instacron:TECPAR10.1590/1678-4324-2022210648info:eu-repo/semantics/openAccessLocatelli,Marcelo SartoriCunha,Evandro L. T. PGuiginski,JanaínaFranco,Ramon A. SBernardes,TerezaAlzamora,Pedro LouresSilva,Daniel Victor F. daGanem,Marcelo Augusto SSantos,Thiago H. MCarvalho,Anne I. RSouza,Leandro M. VPaixão,Gabriela P. FChaves,Elisa FrançaSantos,Guilherme Bezerra dosSantos,Rafael Vinícius dosFreitas,Amanda Cupertino deFlores,Matheus GBiezuner,Rachel FCardoso,Rodolfo LinsFonseca,Rodrigo MachadoSilva,Ana Paula Couto daMeira Jr,Wagnereng2022-04-14T00:00:00Zoai:scielo:S1516-89132022000100314Revistahttps://www.scielo.br/j/babt/https://old.scielo.br/oai/scielo-oai.phpbabt@tecpar.br||babt@tecpar.br1678-43241516-8913opendoar:2022-04-14T00:00Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)false |
dc.title.none.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 Locatelli,Marcelo Sartori Google Trends infodemiology epidemiological predictions digital health |
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 |
Locatelli,Marcelo Sartori |
author_facet |
Locatelli,Marcelo Sartori Cunha,Evandro L. T. P Guiginski,Janaína Franco,Ramon A. S Bernardes,Tereza Alzamora,Pedro Loures Silva,Daniel Victor F. da Ganem,Marcelo Augusto S Santos,Thiago H. M Carvalho,Anne I. R Souza,Leandro M. V Paixão,Gabriela P. F Chaves,Elisa França Santos,Guilherme Bezerra dos Santos,Rafael Vinícius dos Freitas,Amanda Cupertino de Flores,Matheus G Biezuner,Rachel F Cardoso,Rodolfo Lins Fonseca,Rodrigo Machado Silva,Ana Paula Couto da Meira Jr,Wagner |
author_role |
author |
author2 |
Cunha,Evandro L. T. P Guiginski,Janaína Franco,Ramon A. S Bernardes,Tereza Alzamora,Pedro Loures Silva,Daniel Victor F. da Ganem,Marcelo Augusto S Santos,Thiago H. M Carvalho,Anne I. R Souza,Leandro M. V Paixão,Gabriela P. F Chaves,Elisa França Santos,Guilherme Bezerra dos Santos,Rafael Vinícius dos Freitas,Amanda Cupertino de Flores,Matheus G Biezuner,Rachel F Cardoso,Rodolfo Lins Fonseca,Rodrigo Machado Silva,Ana Paula Couto da Meira Jr,Wagner |
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 |
Locatelli,Marcelo Sartori Cunha,Evandro L. T. P Guiginski,Janaína Franco,Ramon A. S Bernardes,Tereza Alzamora,Pedro Loures Silva,Daniel Victor F. da Ganem,Marcelo Augusto S Santos,Thiago H. M Carvalho,Anne I. R Souza,Leandro M. V Paixão,Gabriela P. F Chaves,Elisa França Santos,Guilherme Bezerra dos Santos,Rafael Vinícius dos Freitas,Amanda Cupertino de Flores,Matheus G Biezuner,Rachel F Cardoso,Rodolfo Lins Fonseca,Rodrigo Machado Silva,Ana Paula Couto da Meira Jr,Wagner |
dc.subject.por.fl_str_mv |
Google Trends infodemiology epidemiological predictions digital health |
topic |
Google Trends infodemiology epidemiological predictions digital health |
description |
Abstract: 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.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100314 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100314 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-4324-2022210648 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
dc.source.none.fl_str_mv |
Brazilian Archives of Biology and Technology v.65 2022 reponame:Brazilian Archives of Biology and Technology instname:Instituto de Tecnologia do Paraná (Tecpar) instacron:TECPAR |
instname_str |
Instituto de Tecnologia do Paraná (Tecpar) |
instacron_str |
TECPAR |
institution |
TECPAR |
reponame_str |
Brazilian Archives of Biology and Technology |
collection |
Brazilian Archives of Biology and Technology |
repository.name.fl_str_mv |
Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar) |
repository.mail.fl_str_mv |
babt@tecpar.br||babt@tecpar.br |
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1750318281361719296 |