基于时间融合变压器的预测,使用环境指标预测COVID-19感染趋势
Hannah Mae Portus1, Min Jeong Ban2, Keugtae Kim3
1Department of Civil and Environmental Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea.
Journal of hazardous materials
|February 7, 2026
概括
这项研究使用时间融合变压器 (TFT) 模型通过结合环境数据来预测COVID-19病例,提高了预测率17%. 对病例数的间接估计显示出更高的准确性,证明了深度学习.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 随着COVID-19的爆发,人们越来越需要先进的传染病监测.
- 预测建模和时间序列分析对于预测疾病趋势和为公共卫生战略提供信息至关重要.
研究的目的:
- 开发和评估一个多变量时间序列预测模型,用于预测COVID-19病例动态.
- 评估环境因素 (废水质量,空气质量,天气) 对COVID-19传播预测的影响.
主要方法:
- 开发一个时间融合变压器 (TFT) 模型,其中包含静态和时间变量的变量.
- 利用了2020年2月至2022年5月的地区级COVID-19病例数据和环境变量.
- 将直接病例数预测与基于比例变化的间接估计进行了比较.
主要成果:
- 纳入环境变量使TFT模型的预测性能提高了17%.
- 该模型捕获了COVID-19数据的非静止性和时间依赖性 (R2 = 0.962).
- 与直接预测相比,对病例数的间接估计产生了更高的预测准确度 (R2 = 0.984).
结论:
- 深度学习方法,特别是TFT模型,为流行病学预测提供了重要的价值.
- 环境数据集成提高了传染病趋势预测的准确性.
- 准确的预测支持及时的公共卫生干预和优化资源分配.
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