Preprint Open Access

Forecasting the COVID-19 epidemic integrating symptom search behavior: an infodemiology study

Alessandro Rabiolo; Eugenio Alladio; Esteban Morales; Andrew Ian McNaught; Francesco Bandello; Abdelmonem A Afifi; Alessandro Marchese

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 (

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