Abstract
BackgroundGoogle 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. 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. In this study, we aimed to apply statistically more favorable methods to validate the earlier GT-based COVID-19 study results.MethodsWe 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 addition, the impact of media coverage on keywords with significant Granger-causality was further evaluated using Japanese regional data.ResultsOur 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. “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).ConclusionsOur 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.
Highlights
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
Based on the above analytical concerns for earlier studies, by using the vector autoregression (VAR) model [11,12,13], which is designed to deal with time-series data and is robust against weakness as observed in case of using correlation, we aim to identify statistically more reliable symptom keywords for which GT trends may be used as a predictive measure for future COVID-19 positivity trends, and to validate the earlier study results
COVID-19 data and Google Trends (GT) data were separately analyzed in nine different regions: Japan (JP) and eight English-speaking countries, namely, Australia (AU), Canada (CA), Great IE Ireland (Britain) (GB), Ireland (IE), JP Japan (India) (IN), Singapore (SG), United States (US), and South false discovery rate (FDR) False discovery rate (Africa) (ZA)
Summary
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. 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. Pearson (or Spearman’s rank) correlation is often applied to assess the correlation between the time-series trends of COVID-19 cases/deaths and GT trends in symptom keywords without confirming the stationarity of these time series This is sometimes critically inappropriate in the context of time-series analyses because time-series data often contains unit-root and the correlation between such series often results in high coefficient value and t-statistics [14], and it can increase the likelihood of obtaining spurious correlations. Because COVID-19 and its symptoms have attracted intensive attention worldwide, the influence of media coverage on GT symptom keywords is inevitable [10, 15, 16], which has hardly been adjusted in a statistically favorable manner
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