PDF(Pubmed)
Abstract：
The COVID-19 virus has impacted all facets of our lives. As a global response to this threat, vaccination programmes have been initiated and administered in numerous nations. The question remains, however, as to whether mass vaccination programmes result in a decrease in the number of confirmed COVID-19 cases. In this study, we aim to predict the future number of COVID-19 confirmed cases for the top ten countries with the highest number of vaccinations in the world. A well-known Deep Learning method for time series analysis, namely, the Long Short-Term Memory (LSTM) networks, is applied as the prediction method. Using three evaluation metrics, i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), we found that the model built by using LSTM networks could give a good prediction of the future number and trend of COVID-19 confirmed cases in the considered countries. Two different scenarios are employed, namely: \'All Time\', which includes all historical data; and \'Before Vaccination\', which excludes data collected after the mass vaccination programme began. The average MAPE scores for the \'All Time\' and \'Before Vaccination\' scenarios are 5.977% and 10.388%, respectively. Overall, the results show that the mass vaccination programme has a positive impact on decreasing and controlling the spread of the COVID-19 disease in those countries, as evidenced by decreasing future trends after the programme was implemented.
摘要：
COVID-19病毒影响了我们生活的方方面面。作为对这一威胁的全球回应，疫苗接种计划已经在许多国家启动和实施。问题仍然存在，然而,关于大规模疫苗接种计划是否导致确诊COVID-19病例数量的减少。在这项研究中,我们的目标是预测世界上接种疫苗数量最多的十大国家未来的COVID-19确诊病例数量。一种众所周知的用于时间序列分析的深度学习方法，即,长短期记忆(LSTM)网络，作为预测方法。使用三个评估指标，即，平均绝对误差(MAE)，均方根误差(RMSE),和平均绝对百分比误差(MAPE)，我们发现,使用LSTM网络建立的模型可以很好地预测所考虑国家的COVID-19确诊病例的未来数量和趋势.采用了两种不同的方案，即:\'所有时间\',其中包括所有历史数据；和“疫苗接种前”，这不包括大规模疫苗接种计划开始后收集的数据。“所有时间”和“疫苗接种前”场景的平均MAPE分数分别为5.977％和10.388%，分别。总的来说,结果表明，大规模疫苗接种计划对减少和控制COVID-19疾病在这些国家的传播具有积极影响，方案实施后，未来趋势有所下降。
