Machine Learning-Driven COVID-19 Hospitalization Forecasting: From Theory to Practice in a Major Northeastern Academic Medical Center.

Background: Predicting seasonal and emerging waves of respiratory viruses is crucial for effective public health responses. Despite significant efforts in developing coronavirus disease 2019 (COVID-19) forecast models, there remains a need for improvement in model performances.

Methods: We developed and evaluated a machine learning model to forecast COVID-19 hospitalizations by extending the Neural Basis Expansion Analysis for Time Series Forecasting (N-BEATS) architecture. Specifically, we integrated a temporal convolutional network to incorporate exogenous variables and added additional residual blocks to create a variance-forecasting network component for probabilistic predictions. We compared the performance of our model to the ensemble models from the COVID-19 Forecast Hub. Additionally, we implemented the model in a large academic medical center, applying transfer learning to adapt the model to local hospitalization data.

Results: Our model demonstrated a 34.0% improvement in mean absolute error over the performance-weighted ensemble and 37.0% over the unweighted ensemble in predicting total US hospitalizations. Similar trends were obtained using mean absolute percent error and symmetric mean absolute percent error. In a real-world implementation, the model provided actionable forecasts for hospital leadership to optimize resource allocation and surge preparation.

Conclusions: The enhanced architecture significantly improves the forecasting of COVID-19 hospitalizations, particularly in anticipating peaks and resurgences. Its successful implementation in a hospital system highlights its potential for aiding decision-making and resource planning during pandemics and other respiratory disease outbreaks.