根据百度指数和云南的环境数据预测发病率:深度学习模型研究
Xin Xiong1, Linghui Xiang1, Litao Chang2
1Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
Journal of medical Internet research
|February 6, 2025
概括
这项研究开发了一种整合百度搜索数据和环境因素的预测模型,以预测中国云南的病发病率. 该模型有助于早期发现病爆发和公共卫生响应.
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:
- 在全球范围内,疹疫情正在重新出现,在中国,特别是云南省,这对公众健康构成了重大挑战.
- 传统的监测方法不足以及时准确地预测病爆发.
- 改善预测对于有效的公共卫生干预至关重要.
研究的目的:
- 开发云南省疹发病率的预测模型.
- 为了利用百度搜索索引和环境数据进行增强的病预测.
- 改善早期检测和对疹疫情的反应.
主要方法:
- 发病率的时间序列分析,百度搜索索引和环境因素 (2016-2023年).
- 使用长期短期记忆 (LSTM) 网络开发预测模型.
- 通过使用分布式非线性滞后模型 (DNLM) 的皮尔森相关性和滞后分析来选择特征.
主要成果:
- 综合发病率,百度指数和环境数据的IBE模型显示出最好的预测性能 (R2=0.72).
- 百度搜索索引值与病发病率正相关.
- 在温度和发病发病率之间观察到非线性关联.
结论:
- 模型IBE提供了一个有前途的工具,用于预测云南省发病率.
- 整合搜索引擎数据和环境因素可以提高病预测能力.
- 这种方法可以改善公共卫生监测,并促进对疹疫情的快速反应.
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