COVID-19 Hospitalizations Forecasts Using Internet Search Data

• URL: http://arxiv.org/abs/2202.03869v1
• Date: Thu, 3 Feb 2022 21:56:20 GMT
• Title: COVID-19 Hospitalizations Forecasts Using Internet Search Data
• Authors: Tao Wang, Simin Ma, Soobin Baek, Shihao Yang
• Abstract summary: We extend previously-proposed influenza tracking model, ARGO, to predict future 2-week national and state-level COVID-19 new hospital admissions. Our method achieves on average 15% error reduction over the best alternative models collected from COVID-19 forecast hub.
• Score: 4.748730334762718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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