Forecasting the COVID-19 epidemic by integrating symptom search behavior into predictive models: Infoveillance study

Alessandro Rabiolo, Eugenio Alladio, Esteban Morales, Andrew Ian McNaught, Francesco Bandello, Abdelmonem A. Afifi, Alessandro Marchese

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Background: Previous 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. Objective: The aim of this study is to investigate the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. We aim to develop predictive models to forecast the COVID-19 epidemic based on a combination of Google Trends searches of symptoms and conventional COVID-19 metrics. Methods: An open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal component analysis (PCA) and time series modeling. The application facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected the data of nine 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). The predictive abilities of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only. Results: The 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 search terms and COVID-19 traditional metrics performed better than those not including Google searches (median 1.56, IQR 0.90-2.49 versus median 1.87, IQR 1.09-2.95, respectively), but the improvement in prediction varied as a function of the selected country and time frame. 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 0.90, IQR 0.50-1.53 versus median 2.27, IQR 1.62-3.74, respectively). Conclusions: The inclusion of digital online searches in statistical models may improve the nowcasting and forecasting of the COVID-19 epidemic and could be used as one of the surveillance systems of COVID-19 disease. We provide a free web application operating with nearly real-time data that anyone can use to make predictions of outbreaks, improve estimates of the dynamics of ongoing epidemics, and predict future or rebound waves.

Original languageEnglish
Article numbere28876
JournalJournal of Medical Internet Research
Issue number8
StatePublished - Aug 2021
Externally publishedYes


  • Big data
  • COVID-19
  • Coronavirus
  • Digital health
  • Google Trends
  • Infodemiology
  • Infoveillance
  • Predictive models
  • SARS-CoV-2
  • Shiny web application
  • Symptoms
  • Time series


Dive into the research topics of 'Forecasting the COVID-19 epidemic by integrating symptom search behavior into predictive models: Infoveillance study'. Together they form a unique fingerprint.

Cite this