Çınarka, HalitUysal, Mehmet AtillaÇifter, AtillaNiksarlıoğlu, Elif YeldaÇarkoğlu, Aslı2022-01-172022-01-172021Cinarka, 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.https://hdl.handle.net/20.500.12939/2188This 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.eninfo:eu-repo/semantics/openAccessCOVID-19SARS-CoV-2WHO COVID-19Article1111112-s2.0-85111078319Q1WOS:000675273800026Q1