基于深度学习的每日COVID-19病例预测使用X (Twitter) 数据
Nourhan Ahmed1, Khansa Saeed1, Jeevitha Lora Rodrigues1
1Information Systems and Machine Learning Lab, Department of Mathematics, Natural Science, Economics and Computer Science, Institute of Computer Science, University of Hildesheim.
Studies in health technology and informatics
|August 23, 2024
概括
这项研究使用Twitter数据和深度学习预测了COVID-19的增长率. 整合社交媒体增强了流行病预测和疾病控制工作.
科学领域:
- 计算流行病学计算流行病学
- 社交媒体分析.
- 机器学习用于公共卫生
背景情况:
- 控制COVID-19需要创新的预测方法.
- 社交网络数据为实时疾病监测提供了一个有希望的途径.
- 现有的流行病学模型可以通过结合各种数据流来增强.
研究的目的:
- 用Twitter数据和深度学习来预测确诊的COVID-19病例.
- 评估时间序列混合器 (TSMixer) 模型对多变量时间序列预测的有效性.
- 评估社交媒体数据在流行病学预测方面的潜力.
主要方法:
- 使用自然语言处理 (NLP) 从X (Twitter) 提取数据.
- 作为目标变量,利用世界仪的每日COVID-19G值 (增长率).
- 开发和评估一个时间序列混合器 (TSMixer) 深度学习模型.
主要成果:
- 在24个月的G值预测中达到0.0063的平均平方误差 (MSE).
- 使用MinMax规范化,递归特征消除 (RFE) 和聚合方法进行优化预测.
- 在多变量时间序列数据集上证明了模型的有效性.
结论:
- 社交媒体数据,特别是推特,可以显著提高日常COVID-19病例预测.
- 该TSMixer模型显示了准确的流行病学预测的潜力.
- 整合社交网络数据为公共卫生监测和疾病控制提供了有价值的工具.
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