Medicine and Health

COVID-19 predictability in the United States using Google Trends time series

A. Mavragani and K. Gkillas

This paper explores how Google Trends data can predict COVID-19 cases and deaths in the United States, highlighting its significant implications for public health policy. The research was conducted by Amaryllis Mavragani and Konstantinos Gkillas.

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Abstract

This paper explores the predictability of COVID-19 in the United States using Google Trends data. Pearson and Kendall rank correlations were used to analyze the relationship between Google Trends data and COVID-19 cases and deaths. A bias-corrected quantile regression model was employed for predictability analysis. Results show statistically significant correlations and strong predictability of COVID-19 using Google Trends data, suggesting its value in informing public health policy.

Publisher

Scientific Reports

Published On

Nov 26, 2020

Authors

Amaryllis Mavragani, Konstantinos Gkillas

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