The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis

dc.authoridNiksarlıoğlu, Elif Yelda/0000-0002-6119-6540
dc.authoridÇınarka, Halit/0000-0002-4910-149X
dc.authoridCifter, Atilla/0000-0002-4365-742X
dc.authoridUYSAL, MEHMET ATILLA/0000-0002-0430-498X
dc.authorwosidNiksarlıoğlu, Elif Yelda/X-7048-2019
dc.authorwosidÇınarka, Halit/AAK-6830-2021
dc.authorwosidÇARKOĞLU, ASLI/ABC-5996-2021
dc.authorwosidÇarkoğlu, Aslı/GWM-7995-2022
dc.authorwosidUYSAL, MEHMET ATILLA/P-1518-2015
dc.contributor.authorÇarkoğlu, Aslı
dc.contributor.authorUysal, Mehmet Atilla
dc.contributor.authorCifter, Atilla
dc.contributor.authorNiksarlioglu, Elif Yelda
dc.contributor.authorCarkoglu, Asli
dc.date.accessioned2023-10-19T15:12:09Z
dc.date.available2023-10-19T15:12:09Z
dc.date.issued2021
dc.department-temp[Cinarka, Halit; Uysal, Mehmet Atilla; Niksarlioglu, Elif Yelda] Univ Hlth Sci Turkey, Yedikule Traing & Res Hosp Chest Dis & Thorac Sur, Istanbul, Turkey; [Cifter, Atilla] Altinbas Univ, Dept Econ, Istanbul, Turkey; [Carkoglu, Asli] Kadir Has Univ, Dept Psychol, Istanbul, Turkeyen_US
dc.description.abstractThis study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r >= 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.en_US
dc.identifier.citation6
dc.identifier.doi10.1038/s41598-021-93836-yen_US
dc.identifier.issn2045-2322
dc.identifier.issue1en_US
dc.identifier.pmid34257381en_US
dc.identifier.scopus2-s2.0-85111078319en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1038/s41598-021-93836-y
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5359
dc.identifier.volume11en_US
dc.identifier.wosWOS:000675273800026en_US
dc.identifier.wosqualityQ1
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherNature Researchen_US
dc.relation.ispartofScientific Reportsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectInternetEn_Us
dc.subjectTrendsEn_Us
dc.subjectToolEn_Us
dc.subjectInternet
dc.subjectTrends
dc.subjectTool
dc.titleThe relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysisen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublication211ed1cb-2559-4c2e-b1b5-9001ccd7c139
relation.isAuthorOfPublication.latestForDiscovery211ed1cb-2559-4c2e-b1b5-9001ccd7c139

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
5359.pdf
Size:
2.2 MB
Format:
Adobe Portable Document Format
Description:
Tam Metin / Full Text