Information Systems and Machine Learning Lab, Department of Mathematics, Natural Science, Economics and Computer Science, Institute of Computer Science, University of Hildesheim.
Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany.
Stud Health Technol Inform. 2024 Aug 22;316:1989-1993. doi: 10.3233/SHTI240824.
Due to the importance of COVID-19 control, innovative methods for predicting cases using social network data are increasingly under attention. This study aims to predict confirmed COVID-19 cases using X (Twitter) social network data (tweets) and deep learning methods. We prepare data extracted from tweets by natural language processing (NLP) and consider the daily G-value (growth rate) as the target variable of COVID-19, collected from the worldometer. We develop and evaluate a time series mixer (TSMixer) predictive model for multivariate time series. The mean squared error (MSE) loss on the test dataset was 0.0063 for 24-month Gvalue prediction when using the MinMax normalization with recursive feature elimination (RFE) and average or min aggregation method. Our findings illuminate the potential of integrating social media data to enhance daily COVID-19 case predictions and are applicable also for epidemiological forecasting purposes.
由于 COVID-19 控制的重要性，使用社交网络数据进行病例预测的创新方法越来越受到关注。本研究旨在使用 X（Twitter）社交网络数据（推文）和深度学习方法预测确诊的 COVID-19 病例。我们通过自然语言处理（NLP）准备从推文中提取的数据，并考虑将每日 G 值（增长率）作为从世界计量器收集的 COVID-19 的目标变量。我们开发并评估了用于多变量时间序列的时间序列混合器（TSMixer）预测模型。在使用 MinMax 归一化和递归特征消除（RFE）以及平均或最小聚合方法时，在测试数据集上 24 个月 G 值预测的均方误差（MSE）损失为 0.0063。我们的研究结果阐明了整合社交媒体数据以增强每日 COVID-19 病例预测的潜力，并且也适用于流行病学预测目的。
