  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.
