COVID-19 Hospitalizations Forecasts Using Internet Search Data

TL;DR

This paper presents a model that uses internet search data to accurately forecast COVID-19 hospitalizations, aiding public health planning by capturing variant surges and providing reliable real-time predictions.

Contribution

The study extends the ARGO influenza tracking model to predict COVID-19 hospitalizations using search data, demonstrating improved accuracy and robustness over existing models.

Findings

Achieved 15% error reduction compared to alternative models.

Effectively captures surges from new COVID-19 variants.

Provides a flexible, self-correcting forecasting tool.

Abstract

As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decision on medical resources allocations such as ICU beds, ventilators, and personnel to prepare for the surge of COVID-19 pandemics. Inspired by the strong association between public search behavior and hospitalization admission, we extended previously-proposed influenza tracking model, ARGO (AutoRegression with GOogle search data), to predict future 2-week national and state-level COVID-19 new hospital admissions. Leveraging the COVID-19 related time series information and Google search data, our method is able to robustly capture new COVID-19 variants' surges, and self-correct at both national and state level. Based on our retrospective out-of-sample evaluation over 12-month comparison period, our method achieves on average 15\% error reduction over the best alternative models collected from COVID-19 forecast hub. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist health-care officials and decision making for the current and future infectious disease outbreak.