基于LSTM的循环神经网络预测了可变气候区的流感病情.
Alfred Amendolara1, Christopher Gowans1, Joshua Barton1
1Department of Biomedical Science, Noorda College of Osteopathic Medicine, Provo, Utah, USA.
Immunity, inflammation and disease
|February 23, 2026
概括
季节性气候模式,而不是绝对的天气,驱动流感类疾病 (ILI) 趋势. 经常性神经网络显示了不同地区一致的流感预测,突出了季节性时间对特定气候变量的重要性.
科学领域:
- 流行病学 流行病学
- 气候学 气候学 气候学
- 数据科学数据科学数据科学
背景情况:
- 流感病毒在全球范围内每年都会引起流行病,对公众健康造成重大负担.
- 了解流感传播的季节性变化至关重要,尤其是在SARS-CoV-2的持续影响下.
- 驱动不同气候区的季节性流感负担的机制仍然不完全理解.
研究的目的:
- 调查不同气候区域对季节性流感类疾病 (ILI) 趋势的影响.
- 分析各种气候变量对流感季节性的预测能力.
- 探索机器学习模型在预测不同生态区流感趋势方面的有效性.
主要方法:
- 利用长期短期记忆 (LSTM) 循环神经网络来预测ILI趋势.
- 从CDC收集的每周LI数据和天气数据 (温度,湿度,风速等) 来自视觉交叉. 从视觉交叉.
- 通过使用来自三个不同的气候区域的数据来训练和评估模型:夏威夷,佛蒙特和内华达.
主要成果:
- 所有地区都表现出强烈的流感季节性,夏威夷显示了最高的绝对ILI值.
- 在所有地区,温度与ILI均显示出中度负相关.
- 佛蒙特州和内华达州的太阳辐射和紫外线指数有中等的相关性,但夏威夷的相关性很弱. 跨区域模型的性能相当于基线预测.
结论:
- 气候变量对ILI趋势的预测能力较弱至中等.
- 在不同地区,LSTM模型的表现均,这表明季节性模式是ILI的关键驱动因素.
- 气候的相对季节性变化似乎比绝对气候变量对流感趋势的影响更大.
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