RESUMO
BackgroundPrevious studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions. MethodsAn open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal components analysis (PCA) and time series modelling. The app facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected data of eight countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (Error Trend Seasonality, Autoregressive integrated moving average, and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root-mean-square error (RMSE) of the first principal component (PC1). Predictive ability of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only. FindingsThe degree of correlation and the best time-lag varied as a function of the selected country and topic searched; in general, the optimal time-lag was within 15 days. Overall, predictions of PC1 based on both searched termed and COVID-19 traditional metrics performed better than those not including Google searches (median [IQR]: 1.43 [0.74-2.36] vs. 1.78 [0.95-2.88], respectively), but the improvement in prediction varied as a function of the selected country and timeframe. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median [IQR]: 0.74 [0.47-1.22] vs. 2.15 [1.55-3.89], respectively). InterpretationThe inclusion of digital online searches in statistical models may improve the prediction of the COVID-19 epidemic. FundingEOSCsecretariat.eu has received funding from the European Unions Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644.
