Accurate estimation of influenza epidemics using Google search data via ARGO

TL;DR

The paper introduces ARGO, a new influenza tracking model that leverages publicly available Google search data to provide accurate, real-time epidemic estimates, outperforming previous models including Google Flu Trends.

Contribution

The paper presents ARGO, a novel statistical model that effectively utilizes low-quality online search data for real-time influenza tracking, surpassing existing Google-based models.

Findings

ARGO outperforms Google Flu Trends in accuracy.

It effectively captures seasonality and behavioral changes.

The model is flexible, robust, and scalable.

Abstract

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.