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使用昆明的气象因素预测类似流感的疾病发病率:深度学习模型研究

Pei-Long Li^1,2, Rong-Wei Huang^2,3, Rong-Man Xie⁴

¹Department of pulmonary and critical care medicine, Yunnan Key laboratory of Children's Major Disease Research, Kunming Children's Hospital, Kunming, China.

BMC public health

|August 16, 2025

概括

气象数据和先进的长期短期记忆 (LSTM) 模型,加上核心注意网络 (KAN) 的增强,显著改善了类似流感的疾病 (ILI) 发病率预测. 这种整合为公共卫生流行病预测提供了有价值的工具.

科学领域:

流行病学流行病学
数据科学数据科学数据科学
气象学天气学

背景情况:

全球类似流感的疾病 (ILI) 发病率呈现越来越大的趋势.
气候变化需要对影响流行病预测的气象因素进行研究.
准确的ILI预测对于公共卫生准备至关重要.

研究的目的:

通过使用长短期记忆 (LSTM) 模型,评估气象因素在提高ILI发病率预测方面的有效性.
为开发流行病预测公共卫生应用提供参考.
探索先进的LSTM架构,以提高预测准确度.

主要方法:

从2017年11月到2022年1月收集的ILI发生率和气象数据.
利用皮尔森相关性来分析气象数据和ILI发生率之间的关系.
开发并测试了各种LSTM模型,包括气象数据和无气象数据,并结合了Kernel Attention Network (KAN) 来增强非线性学习.

主要成果:

确定LI发病率和气象变量的周期性变化.
结合气象数据的LSTM模型显示,平均绝对百分比误差 (MAPE) 降低了.
基于注意力的堆叠LSTM实现了30.74%的MAPE,使用KAN.AN进一步提高了性能.

结论:

堆叠的LSTM架构和KAN集成增强了模型表示能力.
利用先进的LSTM模型利用气象数据,可以更准确地预测LI的发生率.
这些发现支持开发可靠的公共卫生工具,用于流行病预测.

关键词:

这是一种类似流感的疾病.这就是为什么KAN KAN KAN KAN.这是LSTM的LSTM.气象学天气学预测预测预测

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