Accurate estimation of influenza epidemics using Google search data via ARGO
By Shihao Yang, Mauricio Santillana, S. C. Kou. Proceedings of the National Academy of Sciences of the United States of America, 2015
363 citations recorded by the source corpus.
Significance 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. 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.
Subfields: Epidemiology, Modeling and Simulation
View Paper: https://www.semanticscholar.org/paper/c312d6f92c16762551d001785393d9102aae0e7b
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Citations in the corpus
- Model Selection Techniques: An Overview (2018, background): “Under various conditions, such as fixed design or random design matrices, consistency of LASSO in estimating the significant variables has been widely studied under such various technical conditions as sparsity, restricted isometry [74], mutual coherence [75], irrepresentable condition [76], and res”
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