ArticleScientific reports2021
The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study.Journal of medical Internet research · 2024Article
- Modeling the Impacts of Governmental and Human Responses on COVID-19 Spread Using Statistical Machine Learning.International journal of digital earth · 2024Article
- The evolution of the COVID-19 pandemic through the lens of google searches.Scientific reports · 2023Article
- Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review.International journal of environmental research and public health · 2022Review
- Correlation between national surveillance and search engine query data on respiratory syncytial virus infections in Japan.BMC public health · 2022Article
- Social Media, Public Health, and Community Mitigation of COVID-19: Challenges, Risks, and Benefits.Journal of medical Internet research · 2022Article
- Nonlinear frequency analysis of COVID-19 spread in Tokyo using empirical mode decomposition.Scientific reports · 2022Article
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Authors and funding
5 authors.
Funding
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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.
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