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

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dc.authorid0000-0002-4365-742Xen_US
dc.contributor.authorÇınarka, Halit
dc.contributor.authorUysal, Mehmet Atilla
dc.contributor.authorÇifter, Atilla
dc.contributor.authorNiksarlıoğlu, Elif Yelda
dc.contributor.authorÇarkoğlu, Aslı
dc.date.accessioned2022-01-17T06:52:29Z
dc.date.available2022-01-17T06:52:29Z
dc.date.issued2021en_US
dc.departmentFakülteler, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Ekonomi Bölümüen_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 signifcant. 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 frst wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specifc 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 frst wave of the COVID-19 pandemic. Illness-specifc 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.citationCinarka, H., Uysal, M. A., Cifter, A., Niksarlioglu, E. Y., & Çarkoğlu, A. (2021). The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis. Scientific Reports, 11(1), 1-11.en_US
dc.identifier.endpage11en_US
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85111078319
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2188
dc.identifier.volume11en_US
dc.identifier.wosWOS:000675273800026
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorÇifter, Atilla
dc.language.isoen
dc.publisherNatureen_US
dc.relation.ispartofScientific Reports
dc.relation.isversionof10.1038/s41598-021-93836-yen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCOVID-19en_US
dc.subjectSARS-CoV-2en_US
dc.subjectWHO COVID-19en_US
dc.titleThe relationship betweenGoogle search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis
dc.typeArticle

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