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

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 Aug...

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Autores principales:	Halit Cinarka, Mehmet Atilla Uysal, Atilla Cifter, Elif Yelda Niksarlioglu, Aslı Çarkoğlu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d9cda2092b5a40989de24cd7ca892f94
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spelling	oai:doaj.org-article:d9cda2092b5a40989de24cd7ca892f942021-12-02T15:33:12ZThe relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis10.1038/s41598-021-93836-y2045-2322https://doaj.org/article/d9cda2092b5a40989de24cd7ca892f942021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93836-yhttps://doaj.org/toc/2045-2322Abstract 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.Halit CinarkaMehmet Atilla UysalAtilla CifterElif Yelda NiksarliogluAslı ÇarkoğluNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Halit Cinarka Mehmet Atilla Uysal Atilla Cifter Elif Yelda Niksarlioglu Aslı Çarkoğlu The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis
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.
format	article
author	Halit Cinarka Mehmet Atilla Uysal Atilla Cifter Elif Yelda Niksarlioglu Aslı Çarkoğlu
author_facet	Halit Cinarka Mehmet Atilla Uysal Atilla Cifter Elif Yelda Niksarlioglu Aslı Çarkoğlu
author_sort	Halit Cinarka
title	The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis
title_short	The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis
title_full	The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis
title_fullStr	The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis
title_full_unstemmed	The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis
title_sort	relationship between google search interest for pulmonary symptoms and covid-19 cases using dynamic conditional correlation analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d9cda2092b5a40989de24cd7ca892f94
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The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis

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