使用多输入多输出的手足口病多步预测模型的评估:中国成都的一个案例研究
Xiaoran Geng1, Yue Ma1, Wennian Cai1
1West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
PLoS neglected tropical diseases
|September 8, 2023
概括
准确的手足口腔疾病 (HFMD) 预测对儿童健康至关重要. Seq2Seq-Shih模型有效地预测HFMD病例和疫情,使用环境数据提前15天.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 环境科学 环境科学
背景情况:
- 手足口腔疾病 (HFMD) 对儿童构成重大公共卫生风险.
- 有效的预防和控制策略需要准确的多天预测和HFMD病例的早期峰值检测.
- 现有的研究往往将预测限制在一天内,从而减少了响应灵活性.
研究的目的:
- 开发和评估HFMD发病率的多步预测模型.
- 评估深度学习模型在预测HFMD病例和检测爆发峰值方面的表现.
- 调查环境因素与HFMD趋势之间的关系.
主要方法:
- 2011-2017年在成都收集的每日HFMD病例数据 (儿童0-14岁) 和环境数据 (气象和空气污染物).
- 使用LSTM,Seq2Seq,Seq2Seq-Luong和Seq2Seq-Shih模型进行多输入,多输出HFMD预测.
- 基于预测准确度,峰值检测时间延迟和强度的评估模型.
主要成果:
- 成都的HFMD表现出与温度,气压,降雨量,湿度和PM10相关的季节性模式.
- Seq2Seq-Shih模型表现出卓越的性能,RMSE,sMAPE和PCC值表明2至15天预测的高精度.
- Seq2Seq-Shih有效地检测到未来的HFMD峰值,时间延迟最小.
结论:
- 深度学习的Seq2Seq-Shih模型对于多步骤的HFMD预测非常有效.
- 该模型准确预测HFMD病例,并通过整合环境因素来识别疫情峰值.
- Seq2Seq-Shih模型为改善对HFMD的公共卫生反应提供了一个有价值的工具.
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