COVID-19 Hospitalizations Forecasts Using Internet Search Data
Tao Wang, Simin Ma, Soobin Baek, Shihao Yang

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…
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Taxonomy
TopicsData-Driven Disease Surveillance · Influenza Virus Research Studies · COVID-19 epidemiological studies
