Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Snap Inc., Machine Learning Research Team, Seattle, WA, USA.
Comput Math Methods Med. 2021 Feb 25;2021:6927985. doi: 10.1155/2021/6927985. eCollection 2021.
COVID-19 has led to a pandemic, affecting almost all countries in a few months. In this work, we applied selected deep learning models including multilayer perceptron, random forest, and different versions of long short-term memory (LSTM), using three data sources to train the models, including COVID-19 occurrences, basic information like coded country names, and detailed information like population, and area of different countries. The performances of the models are measured using four metrics, including mean average percentage error (MAPE), root mean square error (RMSE), normalized RMSE (NRMSE), and . The best performance was found for a modified version of LSTM, called M-LSTM (winner model), to forecast the future trajectory of the pandemic in the mentioned countries. For this purpose, we collected the data from January 22 till July 30, 2020, for training, and from 1 August 2020 to 31 August 2020, for the testing phase. Through experimental results, the winner model achieved reasonably accurate predictions (MAPE, RMSE, NRMSE, and are 0.509, 458.12, 0.001624, and 0.99997, respectively). Furthermore, we stopped the training of the model on some dates related to main country actions to investigate the effect of country actions on predictions by the model.
新冠疫情已经引发了一场大流行，在短短几个月内几乎影响了所有国家。在这项工作中，我们应用了选定的深度学习模型，包括多层感知机、随机森林和不同版本的长短时记忆网络（LSTM），使用三个数据源来训练模型，包括新冠疫情的发生情况、编码国家名称等基本信息以及不同国家的人口和面积等详细信息。我们使用四个指标来衡量模型的性能，包括平均百分比误差（MAPE）、均方根误差（RMSE）、归一化 RMSE（NRMSE）和 。我们发现，经过修改的 LSTM 版本（称为 M-LSTM，即优胜模型）在预测上述国家未来疫情轨迹方面表现最佳。为此，我们从 2020 年 1 月 22 日到 7 月 30 日收集数据进行训练，并从 2020 年 8 月 1 日到 8 月 31 日进行测试阶段。通过实验结果，优胜模型实现了相当准确的预测（MAPE、RMSE、NRMSE 和 分别为 0.509、458.12、0.001624 和 0.99997）。此外，我们在与主要国家行动相关的一些日期停止了模型的训练，以研究国家行动对模型预测的影响。
