The Coronavirus Disease 2019 or the COVID-19 pandemic has swept almost all parts of the world since the first case was found in Wuhan, China, in December 2019. With the increasing number of COVID-19 cases in the world, SARS-CoV-2 has mutated into various variants. Given the increasingly dangerous conditions of the pandemic, it is crucial to know when the pandemic will stop by predicting confirmed cases of COVID-19. Therefore, many studies have raised COVID-19 as a case study to overcome the ongoing pandemic using the Deep Learning method, namely LSTM, with reasonably accurate results and small error values. LSTM training is used to predict confirmed cases of COVID-19 based on variants that have been identified using ECDC's COVID-19 dataset containing confirmed cases of COVID-19 that have been identified from 30 countries in Europe. Tests were conducted using the LSTM and BiLSTM models with the addition of RNN as comparisons on hidden size and layer size. The obtained result showed that in testing hidden sizes 25, 50, 75 to 100, the RNN model provided better results, with the minimum MSE value of 0.01 and the RMSE value of 0.012 for B.1.427/B.1.429 variant with hidden size 100. In further testing of layer sizes 2, 3, 4, and 5, the result shows that the BiLSTM model provided better results, with minimum MSE value of 0.01 and the RMSE of 0.01 for the B.1.427/B.1.429 variant with hidden size 100 and layer size 2.

翻译：自2019年12月中国武汉发现首例病例以来，新冠病毒病（COVID-19）已席卷全球几乎全部地区。随着全球确诊病例数量持续攀升，SARS-CoV-2已突变为多种变异株。鉴于疫情形势日益严峻，通过预测确诊病例数掌握疫情终结时间至关重要。为此，多项研究采用深度学习模型（如LSTM）针对COVID-19展开案例分析，并取得了较高精度与较小误差的预测结果。本研究基于欧洲疾病预防控制中心（ECDC）涵盖欧洲30个国家确诊病例的COVID-19数据集，利用LSTM训练模型对已鉴定的病毒变异株进行确诊病例预测。实验采用LSTM与BiLSTM模型，并引入RNN作为隐藏层规模与层数设置的对比模型。结果表明：在隐藏层规模25、50、75至100的测试中，RNN模型在B.1.427/B.1.429变异株（隐藏层100）上取得更优性能，最小均方误差（MSE）达0.01，均方根误差（RMSE）为0.012。在后续层数（2、3、4、5）测试中，BiLSTM模型在B.1.427/B.1.429变异株（隐藏层100，层数2）上表现更佳，最小MSE与RMSE值均为0.01。