Signals and Communications Department (DSC), University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017, Las Palmas de Gran Canaria, Spain.
Sci Rep. 2024 Oct 29;14(1):25982. doi: 10.1038/s41598-024-69319-1.
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), R, 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.
2019 年 COVID-19 大流行的出现及其在全球范围内的迅速传播，使世界各地的医疗保健系统经受了考验。这场危机给医院带来了前所未有的压力，使医疗保健管理的复杂任务更加恶化。结果，导致死亡率的悲惨增加，并突出了迫切需要先进的预测工具来支持决策。为了应对这些关键挑战，本研究旨在开发和实施一个预测系统，该系统能够以高精度（以平均绝对误差（MAE）、均方根误差（RMSE）、R 和平均绝对百分比误差（MAPE）衡量）和低计算及经济成本来预测大流行的演变。它使用一组相互连接的长短期记忆（LSTM）与双双向 LSTM（BiLSTM）层一起，使用基于未来时间窗口的新颖预处理方法。该模型仅使用 40%的数据集进行训练，即可长时间准确预测 COVID-19 病例和医院占用率。这导致了长期预测，我们可以每天查询未来三天的病例，所需数据非常少。本分析中使用的数据来自西班牙大加那利岛的“Insular 医院”。这些数据描述了冠状病毒病（COVID-19）从 2020 年初首次出现到 2022 年 3 月 29 日的传播情况。结果表明，与具有类似条件的其他研究相比，MAE（<161）、RMSE（<405）和 MAPE（>0.20）有所改善。这将是医疗保健系统的有力工具，为决策者提供有价值的信息，使他们能够预测和制定可能的方案，最终改善公共卫生结果并优化医疗保健和经济资源的分配。
