Deep Learning Based Forecasting of COVID-19 Hospitalisation in England: A Comparative Analysis

In the midst of the COVID-19 pandemic, it was essential to accurately forecast the demand for hospitalisation resources to achieve an effective allocation of healthcare resources. This paper explores the potential of various Deep Learning (DL) models, namely basic Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRU), Bidirectional RNNs, and Sequence-to-Sequence architectures with the inclusion of attention mechanisms, to forecast the demand for hospitalisation resources (mechanical ventilators) in England during the COVID-19 pandemic. The implementation of simulated annealing (SA) as a hyperparameter tuning method produced certain model structures and good results in terms of prediction accuracy. Our findings show that the LSTM-based models (LSTM_SA), achieved the lowest mean average error (MAE), outperforming other architectures used in this study. The results of this study show the potential of DL models to forecast the demand for resources and could help inform the distribution of hospitalisation resources in England during the COVID-19 pandemic.