Novel cost-effective method for forecasting COVID-19 and hospital occupancy using deep learning

Abstract

The emergence of the COVID-19 pandemic in 2019 and its rapid global spread put healthcare systems around the world to the test. This crisis created an unprecedented level of stress in hospitals, exacerbating the already complex task of healthcare management. As a result, it led to a tragic increase in mortality rates and highlighted the urgent need for advanced predictive tools to support decision-making. To address these critical challenges, this research aims to develop and implement a predictive system capable of predicting pandemic evolution with accuracy (in terms of Mean Absolute error (MAE), Root Mean Square Error (RMSE), R2, and Mean Absolute Percentage Error (MAPE)) and low computational and economic cost. It uses a set of interconnected Long Short Term-memory (LSTM) with double bidirectional LSTM (BiLSTM) layers together with a novel preprocessing based on future time windows. This model accurately predicts COVID-19 cases and hospital occupancy over long periods of time using only 40% of the set to train. This results in a long-term prediction where each day we can query the cases for the next three days with very little data. The data utilized in this analysis were obtained from the “Hospital Insular” in Gran Canaria, Spain. These data describe the spread of the coronavirus disease (COVID-19) from its initial emergence in 2020 until March 29, 2022. The results show an improvement in MAE (< 161), RMSE (< 405), and MAPE (> 0.20) compared to other studies with similar conditions. This would be a powerful tool for the healthcare system, providing valuable information to decision-makers, allowing them to anticipate and strategize for possible scenarios, ultimately improving public health outcomes and optimizing the allocation of healthcare and economic resources.