Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada

Background: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. Methods: We analyzed Google Trends (GT) and Twitter data from 1/1/2020 to 3/31/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. Results: Among symptom keywords, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8, showing that searching for these terms on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were r = 0.868 at 11 days earlier and r = 0.840 at 10 days earlier, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients >0.75. Combining GT and Tweet signals did not improve the model performance. Conclusion: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.