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 decisions on medical resources allocations. This paper aims to forecast future 2 weeks national and state-level COVID-19 new hospital admissions in the United States. Our method is inspired by the strong association between public search behavior and hospitalization admissions and is extended from a previously-proposed influenza tracking model, AutoRegression with GOogle search data (ARGO). Our LASSO-penalized linear regression method efficiently combines Google search information and COVID-19 related time series information with dynamic training and rolling window prediction. Compared to other publicly available models collected from COVID-19 forecast hub, our method achieves substantial error reduction in a retrospective out-of-sample evaluation from Jan 4, 2021, to Dec 27, 2021. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist healthcare officials and decision making for the current and future infectious disease outbreaks.
【초록키워드】 COVID-19, public health, Hospitalization, Influenza, Decision making, variant, Infectious disease, Spread, Outbreaks, healthcare, Linear regression, COVID-19 hospitalization, Hospital admission, United States, information, Admission, Critical, association, retrospective, Google, Google search, powerful tool, National, LASSO, The United States, medical resource, robust, flexible, ARGO, collected, globe, reduction in, assist, 【제목키워드】 COVID-19 hospitalization,