Incorporating Connectivity among Internet Search Data for Enhanced Influenza-like Illness Tracking

TL;DR

This paper introduces ARGO-C, a new method that leverages the clustering structure of Internet search data to improve the accuracy and interpretability of influenza-like illness tracking across different regions.

Contribution

The paper presents ARGO-C, a statistically principled approach that incorporates data clustering to enhance disease tracking accuracy and robustness over existing methods.

Findings

ARGO-C outperforms benchmark methods in influenza-like illness tracking.

It demonstrates improved robustness across various geographical resolutions.

The approach is adaptable to track other diseases and social trends.

Abstract

Big data collected from the Internet possess great potential to reveal the ever-changing trends in society. In particular, accurate infectious disease tracking with Internet data has grown in popularity, providing invaluable information for public health decision makers and the general public. However, much of the complex connectivity among the Internet search data is not effectively addressed among existing disease tracking frameworks. To this end, we propose ARGO-C (Augmented Regression with Clustered GOogle data), an integrative, statistically principled approach that incorporates the clustering structure of Internet search data to enhance the accuracy and interpretability of disease tracking. Focusing on multi-resolution %ILI (influenza-like illness) tracking, we demonstrate the improved performance and robustness of ARGO-C over benchmark methods at various geographical resolutions. We also highlight the adaptability of ARGO-C to track various diseases in addition to influenza, and to track other social or economic trends.