在英格兰次区域内预测流感住院病例,使用层次化的通用增剂模型
Jonathon Mellor1, Rachel Christie2, Christopher E Overton2,3
1UK Health Security Agency, Data Analytics and Surveillance, 10 South Colonnade, London, United Kingdom. Jonathon.Mellor@UKHSA.gov.uk.
Communications medicine
|December 20, 2023
概括
一个新的分层通用添加模型 (GAM) 改善了短期流感入院预测. 这种先进的预测工具有助于在季节性流感爆发期间规划医疗保健.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 季节性流感每年对医疗保健系统造成重大负担.
- COVID-19 流行病政策影响了流感传播动态,使复苏预测复杂化.
- 准确预测流感影响对于医疗准备至关重要.
研究的目的:
- 开发和评估一种新的等级性通用添加模型 (GAM),用于短期流感入院预测.
- 评估GAM的预测性能与ARIMA和Prophet等既有时间序列模型相比.
- 为实时流行病预测和医疗保健运营规划提供强大的工具.
主要方法:
- 开发了一个分层的GAM,包括时空线条,周周期和空间相关性,用于英格兰的亚区域性流感住院.
- 使用诸如间隔得分,覆盖率,偏差和中位数绝对误差等指标评估模型性能,用于2022-2023赛季.
- 将分层GAM与自行回归集成移动平均 (ARIMA) 和Prophet时间序列模型进行比较.
主要成果:
- 与ARIMA和Prophet模型相比,分层GAM在所有地理尺度上都表现出卓越的预测性能.
- 在7天和14天的时间范围内观察到预测准确度的提高.
- 模型灵敏度分析表明,国家流行病趋势平滑功能显著影响GAM性能.
结论:
- 引入了一种新的层次,空间和时间建模方法,用于短期流感入院预测.
- 开发的方法适用于实时流行病预测.
- 该建模框架已成功应用于支持2022-2023冬季在英格兰的医疗保健运营规划.
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