The relationship betweenGoogle search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis
Yükleniyor...
Dosyalar
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Nature
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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 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.
Açıklama
Anahtar Kelimeler
COVID-19, SARS-CoV-2, WHO COVID-19
Kaynak
Scientific Reports
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
11
Sayı
1
Künye
Cinarka, 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.
