  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.
