Abstract
In the presence of population-level health threats, precision public health approaches seek to provide the right intervention to the right population at the right time. Accurate real-time surveillance methodologies that can estimate infectious disease activity ahead of official healthcare-based reports, in relevant spatial resolutions, are critical to eventually achieve this goal. We introduce a novel methodological framework for this task which dynamically combines two distinct flu tracking techniques, using ensemble machine learning approaches, to achieve improved flu activity estimates at the state level in the US. The two predictive techniques behind the proposed ensemble methodology, named ARGONet, utilize (1) a dynamic and self-correcting statistical approach to combine flu-related Google search frequencies, information from electronic health records, and historical trends within a given state, as well as (2) a data-driven network-based approach that leverages spatial and temporal synchronicities observed in historical flu activity across states to improve state-level flu activity estimates. The proposed ensemble approach considerably outperforms each individual method and any previously proposed state-specific method for flu tracking, with higher correlations and lower prediction errors.
