在马里科帕县 (AZ) 使用LSTM预测谷热感染的时间序列预测
Xueting Jin1,2, Fangwu Wei1, Srinivasa Srivatsav Kandala1
1Decision Theater, Knowledge Enterprise, Arizona State University, Tempe, AZ, USA.
Lancet regional health. Americas
|February 24, 2025
概括
使用LSTM网络预测Coccidioidomycosis (山谷热) 显示了准确的趋势. 较短的预测期为公共卫生提供了更好的预测.
科学领域:
- 流行病学 流行病学
- 传染病建模 传染病建模
- 人工智能在公共卫生中的作用
背景情况:
- 皮性菌病 (CM) 或山谷热是一种日益严重的呼吸道感染问题.
- 准确预测CM病例对于公共卫生干预和治疗策略至关重要.
研究的目的:
- 使用长短期记忆 (LSTM) 网络预测Coccidioidomycosis (CM) 病例.
- 确定影响CM流行的关键因素,包括COVID-19期间的环境和生活方式变化.
主要方法:
- 利用了LSTM网络与亚利桑那州马里科帕县 (2020-2022) 的每日肺炎病例数据.
- 包含的天气变量 (温度,降水,风速),空气质量 (PM10,PM2.5),干旱指数和COVID-19严格性指数.
- 分析了时间延迟效应和因素与CM流行之间的相关性.
主要成果:
- LSTM模型准确地预测CM流行趋势,平均平方误差 (MSE) 较低.
- 在预测期长度和模型性能之间存在权衡;较短的时间 (10-30天) 显示出更好的准确性.
- 确定了10天和30天CM病例预测的最佳LSTM模型.
结论:
- 与统计方法集成的LSTM算法为预测CM病例提供了强大的工具.
- 预测性见解可以显著帮助公共卫生官员,研究人员和临床医生管理山谷热病.
- 通过准确的CM病例预测,为公共卫生战略提供信息至关重要.
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