COVID-19 Hospitalizations Forecasts Using Internet Search Data
This provides incremental improvement for public health officials needing reliable hospitalization forecasts to allocate medical resources during infectious disease outbreaks.
The researchers tackled the problem of forecasting COVID-19 hospitalizations by extending an existing influenza tracking model (ARGO) to use COVID-19 time series and Google search data, achieving an average 15% error reduction over alternative models in retrospective evaluation.
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.