Accurate estimation of influenza epidemics using Google search data via ARGO

Big data generated from the Internet have great potential in tracking and predicting massive social activities. In this article, we focus on tracking influenza epidemics. We propose a model that utilizes publicly available Google search data to estimate current influenza-like illness activity level. Our model outperforms all available Google-search-based real-time tracking models for influenza epidemics at the national level of the United States, including Google Flu Trends. Our model is flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for estimation and prediction at multiple temporal and spatial resolutions for other social events.