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在沙特阿拉伯分析类似流感的疾病趋势:对统计和深度学习技术的比较研究
1Department of Mathematics, Al-Baha University College of Science, Al-Baha, Saudi Arabia.
Osong public health and research perspectives
|June 12, 2025
概括
长期短期记忆 (LSTM) 深度学习模型在沙特阿拉伯预测类似流感的疾病 (ILI) 的霍尔特-温特斯统计方法的表现明显优于,特别是当包括外部数据时. 这一进步提高了中东流行病学建模的准确性.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 预测季节性类似流感疾病 (ILI) 对中东的公共卫生准备至关重要.
- 传统的统计模型可能无法完全捕捉复杂的传输动态.
研究的目的:
- 开发和评估沙特阿拉伯每周ILI发病率的预测模型.
- 为了比较霍尔特-温特和长期短期记忆 (LSTM) 模型的性能.
- 评估特定区域外源变量的预测附加值.
主要方法:
- 分析了沙特阿拉伯2017-2022年每周的ILI数据.
- 时间序列分析包括气候和人口流动数据.
- 实施了霍尔特-温特斯和LSTM模型并使用RMSE,MAE和R2指标进行了比较.
主要成果:
- 使用外源变量的LSTM模型获得了最高的准确性 (RMSE:28.55,R2:0.96).
- 没有外源变量的LSTM显示出强大的预测能力 (RMSE:34.07,R2:0.93).
- 霍尔特-温特斯模型显示中等性能与显著的不明原因变化 (RMSE: 82.57,R2: 0.58).
结论:
- 深度学习 (LSTM) 与统计方法相比,为ILI预测提供了更高的准确性.
- 纳入外源变量可以提高流行病学模型的预测精度.
- 了解模型的优点和局限性是有效的公共卫生监测的关键.
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