Accurate estimation of influenza epidemics using Google search data via ARGO
This provides public health officials with a more accurate tool for timely influenza tracking, though it is incremental as it builds on existing search-based methods.
The researchers tackled the problem of real-time influenza outbreak tracking by developing ARGO, a model that uses publicly available Google search data, which outperformed all previous Google-search-based tracking models including Google Flu Trends.
Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search-based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people's online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions.