Sensing Social Media to Forecast COVID-19 Cases

Social media has become a key tool for spreading the news, discussing ideas and comments on world events. This is also happening with respect to COVID-19 pandemics. Online platforms can help in monitoring disease occurrences since users self-report their health-related issues. The paper describes a study aiming at evaluating the correlation of tweets with official COVID-19 data. Based on the outcomes of the correlation study, the paper proposes a forecasting model to predict the number of new daily COVID-19 cases. The approach is formulated as an autoregressive model that combines tweets and official COVID-19 data. A real-word dataset of tweets is used for the correlation study and to evaluate the performance of the forecasting model. Results shown the feasibility of the approach, highlighting the improvement obtained when tweets are integrated in the forecasting model, allowing to predict new COVID-19 cases in advance, on average 4-6 days before they were confirmed.