ARGO: a model for accurate estimation of influenza epidemics using Google search data
Year: 2015
Type: preprint
Abstract: Accurate real-time tracking of influenza outbreaks helps public health ocials make timely and meaningful decisions that could save lives. We propose a new 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 tracking models, including the latest version of Google Flu Trends (GFT), 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.
Source: arXiv (Cornell University)
Authors: Shihao Yang, Mauricio Santillana, S. C. Kou
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2163399424