Nabi Khondoker Nazmoon, Tahmid Md Toki, Rafi Abdur, Kader Muhammad Ehsanul, Haider Md Asif
Department of Mathematics, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh.
Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh.
Results Phys. 2021 May;24:104137. doi: 10.1016/j.rinp.2021.104137. Epub 2021 Apr 19.
Though many countries have already launched COVID-19 mass vaccination programs to control the disease outbreak quickly, numerous countries around worldwide are grappling with unprecedented surges of new COVID-19 cases due to a more contagious and deadly variant of coronavirus. As the number of new cases is skyrocketing, pandemic fatigue and public apathy towards different intervention strategies pose new challenges to government officials to combat the pandemic. Henceforth, it is indispensable for the government officials to understand the future dynamics of COVID-19 flawlessly to develop strategic preparedness and resilient response planning. In light of the above circumstances, probable future outbreak scenarios in Brazil, Russia, and the United kingdom have been sketched in this study with the help of four deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN) and multivariate convolutional neural network (MCNN). In our analysis, the CNN algorithm has outperformed other deep learning models in terms of validation accuracy and forecasting consistency. It is unearthed in our study that CNN can provide robust long-term forecasting results in time-series analysis due to its capability of essential features learning, distortion invariance, and temporal dependence learning. However, the prediction accuracy of the LSTM algorithm has been found to be poor as it tries to discover seasonality and periodic intervals from any time-series dataset, which were absent in our studied countries. Our study has highlighted the promising validation of using convolutional neural networks instead of recurrent neural networks when forecasting with very few features and less amount of historical data.
尽管许多国家已经启动了新冠病毒大规模疫苗接种计划，以迅速控制疫情爆发，但由于一种传染性更强、致死性更高的新冠病毒变种，全球众多国家仍在应对前所未有的新增新冠病例激增情况。随着新增病例数量急剧上升，疫情疲劳以及公众对不同干预策略的冷漠态度给政府官员抗击疫情带来了新的挑战。从今往后，政府官员必须完美理解新冠病毒未来的动态，以便制定战略准备和弹性应对计划。鉴于上述情况，本研究借助长短期记忆网络（LSTM）、门控循环单元（GRU）、卷积神经网络（CNN）和多变量卷积神经网络（MCNN）这四种深度学习模型，勾勒了巴西、俄罗斯和英国未来可能出现的疫情爆发情景。在我们的分析中，CNN算法在验证准确性和预测一致性方面优于其他深度学习模型。我们的研究发现，由于CNN具备基本特征学习、失真不变性和时间依赖性学习能力，它能够在时间序列分析中提供可靠的长期预测结果。然而，由于LSTM算法试图从我们所研究国家并不存在的任何时间序列数据集中发现季节性和周期性间隔，因此其预测准确性较差。我们的研究突出表明，在特征极少且历史数据量较少的情况下进行预测时，使用卷积神经网络而非循环神经网络进行验证很有前景。
