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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
Tags
COVID-19
predictability
Google Trends
public health
quantile regression
Pearson correlation
Kendall rank correlation
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