# COVID-19 Hospitalizations Forecasts Using Internet Search Data

arXiv: https://arxiv.org/abs/2202.03869 · HTML: https://mlsanity.com/paper/2202.03869/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [Inter-Series Attention Model for COVID-19 Forecasting](https://mlsanity.com/paper/2010.13006/) (2020)
- [Accurate estimation of influenza epidemics using Google search data via ARGO](https://mlsanity.com/paper/1505.00864/) (2015)

## Abstract (arXiv)

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.