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The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional
Halit Cinarka1, Mehmet Atilla Uysal2, Atilla Cifter3
1Yedikule Training and Research Hospital for Chest Diseases and Thoracic Surgery, University of Health Sciences Turkey, Istanbul, Turkey. halit.cinarka@sbu.edu.tr.
Scientific Reports
|July 14, 2021
Summary
Web searches for COVID-19 symptoms like fever and cough can predict future case numbers. This predictive power, especially using dynamic conditional correlation, helps healthcare systems prepare for outbreaks.
Area of Science:
- Epidemiology
- Public Health
- Digital Health
Background:
- The COVID-19 pandemic necessitated novel methods for tracking disease spread.
- Early detection of outbreaks is crucial for effective public health response and resource allocation.
Purpose of the Study:
- To assess the monitoring and predictive value of web-based symptom searches for COVID-19.
- To compare the efficacy of different correlation models in analyzing search trend data.
Main Methods:
- Utilized Google Trends (GT) data for symptom searches (fever, cough, dyspnea) in five European countries (Jan-Aug 2020).
- Employed dynamic conditional correlation (DCC) and sliding windows correlation models to analyze time-varying correlations between GT searches and new COVID-19 cases.
- Applied a root mean square error (RMSE) approach to determine symptom-specific prediction shifts.
Main Results:
- Significant time-varying correlations were observed between GT searches for pulmonary symptoms and new COVID-19 cases.
- The DCC model demonstrated superior performance over sliding windows correlation, achieving high correlations (r ≥ 0.90) during the first pandemic wave.
- Symptom-specific shifts were identified, indicating pulmonary symptom searches require separate adjustments for accurate prediction.
Conclusions:
- Web-based search interest for COVID-19 pulmonary symptoms serves as a reliable early predictor of reported cases during the initial pandemic wave.
- Leveraging illness-specific symptom search data from platforms like Google Trends can enhance healthcare system preparedness and resource management.
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