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Updated: Sep 11, 2025

High-throughput Detection Method for Influenza Virus

High-throughput Detection Method for Influenza Virus

Published on: February 4, 2012

一个深度学习框架,用于使用搜索引擎数据来预测类似流感的疾病,并区分流行病和非流行病季节:多功能时间序列分析

Ji Li^1,2, Xiangyu Yan¹, Xingjie Chu³

¹School of Disaster and Emergency Medicine, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin, 300072, China, 86 13512065620.

Journal of medical Internet research

|August 11, 2025

概括

这项研究开发了一个使用百度搜索数据和类似流感的疾病 (ILI%) 的深度学习模型,以改善流感流行病预测. 该模型在分离流行病和非流行病季节时显示出更好的预测准确性,提高了公共卫生准备.

科学领域:

公共卫生公共卫生
流行病学流行病学
数据科学数据科学数据科学

背景情况:

季节性流感流行病对全球健康构成重大威胁.
网络搜索数据为预测流行病提供了宝贵的见解.
现有的预测模型往往忽视了搜索术语对季节变化的微妙敏感性.

研究的目的:

为预测流感流行病状态提出一个深度学习框架.
利用百度搜索索引和类似流感的疾病 (ILI%) 数据进行增强的预测.
在各种流行病状态中区分预测准确度.

主要方法:

收集每周的ILI%数据 (2013-2024) 和相应的百度搜索索引.
在搜索查询和ILI%之间进行了交叉相关性分析.
开发和评估一个卷积长期短期记忆 (CLSTM) 网络,预测未来1-3周的情况,比较不同时间段 (所有,流行,非流行) 的表现.

主要成果:

ILI%在中国表现出常规的季节性发病率.
当将数据分为流行病和非流行病季节 (MAPE 10.73%) 与全年期 (MAPE 12.78%) 相比,预测更准确.
集成百度搜索数据的CLSTM模型仅使用ILI%的模型表现优于LI%的模型,并在特定场景中表现出高于LSTM和变压器模型的性能.

结论:

将百度指数与传统监测数据相结合,可以提高流感预测的准确性,尤其是在考虑不同的流行和非流行季节时.
开发的CLSTM框架为改善流感早期预警系统提供了一个有希望的方法.
这项研究为公共卫生准备和及时应对疾病提供了新的视角.

关键词:

百度指数是百度的指数.这是一个CLSTM框架.卷积式的长期短期记忆.流感流感流感流感流感流感流行病是流感流行病.没有流行病的季节.

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