Forecasting the COVID-19 epidemic integrating symptom search behavior: an infodemiology study
Creators
- 1. Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, United Kingdo
- 2. Department of Chemistry, University of Turin, Turin, Italy
- 3. Jules Stein Eye Institute, David Geffen School of Medicine, UCLA, Los Angeles, USA
- 4. Department of Ophthalmology, Vita-Salute University, IRCCS Ospedale San Raffaele Scientific Institute, Milan, Italy
- 5. Department of Biostatistics, Fielding School of Public Health, UCLA, Los Angeles, USA
Description
This study investigates the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. It demonstrates that some of the searched terms showed an unusually high recent online interest that deviated considerably from their expected behavior and anticipated the peak of confirmed COVID-19 cases by days to weeks. This information was used to develop and validat predictive models to forecast COVID-19 epidemic based on the combination of Google Trends searches of symptoms associated with COVID-19 and traditional COVID-19 metrics. Models incorporating Google Trends data performed generally better than those based solely on traditional COVID-19 metrics. The methodology of this study is implemented in an open-access web-application (https://predictpandemic.org)
Files
2021.03.09.21253186v1.full.pdf
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(943.2 kB)
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