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

dc.authorid Niksarlıoğlu, Elif Yelda/0000-0002-6119-6540
dc.authorid Çınarka, Halit/0000-0002-4910-149X
dc.authorid Cifter, Atilla/0000-0002-4365-742X
dc.authorid UYSAL, MEHMET ATILLA/0000-0002-0430-498X
dc.authorwosid Niksarlıoğlu, Elif Yelda/X-7048-2019
dc.authorwosid Çınarka, Halit/AAK-6830-2021
dc.authorwosid ÇARKOĞLU, ASLI/ABC-5996-2021
dc.authorwosid Çarkoğlu, Aslı/GWM-7995-2022
dc.authorwosid UYSAL, MEHMET ATILLA/P-1518-2015
dc.contributor.author Çarkoğlu, Aslı
dc.contributor.author Uysal, Mehmet Atilla
dc.contributor.author Cifter, Atilla
dc.contributor.author Niksarlioglu, Elif Yelda
dc.contributor.author Carkoglu, Asli
dc.contributor.other Psychology
dc.date.accessioned 2023-10-19T15:12:09Z
dc.date.available 2023-10-19T15:12:09Z
dc.date.issued 2021
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, Turkey en_US
dc.description.abstract This 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.citationcount 6
dc.identifier.doi 10.1038/s41598-021-93836-y en_US
dc.identifier.issn 2045-2322
dc.identifier.issue 1 en_US
dc.identifier.pmid 34257381 en_US
dc.identifier.scopus 2-s2.0-85111078319 en_US
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1038/s41598-021-93836-y
dc.identifier.uri https://hdl.handle.net/20.500.12469/5359
dc.identifier.volume 11 en_US
dc.identifier.wos WOS:000675273800026 en_US
dc.identifier.wosquality Q1
dc.khas 20231019-WoS en_US
dc.language.iso en en_US
dc.publisher Nature Research en_US
dc.relation.ispartof Scientific Reports en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 11
dc.subject Internet En_Us
dc.subject Trends En_Us
dc.subject Tool En_Us
dc.subject Internet
dc.subject Trends
dc.subject Tool
dc.title The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis en_US
dc.type Article en_US
dc.wos.citedbyCount 9
dspace.entity.type Publication
relation.isAuthorOfPublication 211ed1cb-2559-4c2e-b1b5-9001ccd7c139
relation.isAuthorOfPublication.latestForDiscovery 211ed1cb-2559-4c2e-b1b5-9001ccd7c139
relation.isOrgUnitOfPublication 9390486a-b1dc-46cf-ad5f-31415f0c8b95
relation.isOrgUnitOfPublication.latestForDiscovery 9390486a-b1dc-46cf-ad5f-31415f0c8b95

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