It is used as one of the “infodemiology” tools to study epidemiological trends of certain disease outbreaks such as the Middle East Respiratory Syndrome epidemic and the Ebola outbreak. Google Trends (GT) is a publicly available source of online Google search trafficking data ( ), which allows users to visualize changes in time series related to the general public’s online interest in certain keywords. Our results suggest that some of the search keywords reported as candidate predictive measures in earlier GT-based COVID-19 studies may potentially be unreliable therefore, caution is necessary when interpreting published GT-based study results. “Sense of smell” and “loss of smell” were the most reliable GT keywords across all the evaluated countries however, when adjusted with their media coverage, these keyword trends did not Granger-cause the COVID-19 positivity trends (in Japan). Our Granger causality-based approach largely decreased (by up to approximately one-third) the number of keywords identified as having a significant temporal relationship with the COVID-19 trend when compared to those identified by Pearson or Spearman’s rank correlation-based approach. In addition, the impact of media coverage on keywords with significant Granger-causality was further evaluated using Japanese regional data. We extracted the relative GT search volume for keywords associated with COVID-19 symptoms, and evaluated their Granger-causality to weekly COVID-19 positivity in eight English-speaking countries and Japan. In this study, we aimed to apply statistically more favorable methods to validate the earlier GT-based COVID-19 study results. However, many of the earlier GT-based studies include potential statistical fallacies by measuring the correlation between non-stationary time sequences without adjusting for multiple comparisons or the confounding of media coverage, leading to concerns about the increased risk of obtaining false-positive results. Google Trends (GT) is being used as an epidemiological tool to study coronavirus disease (COVID-19) by identifying keywords in search trends that are predictive for the COVID-19 epidemiological burden.
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