现在预测方法的应用:英格兰2023/2024年冬季的诺罗病毒病例
Jonathon Mellor1, Maria L Tang1, Emilie Finch1,2
1Chief Data Officer Group, UK Health Security Agency, London, United Kingdom.
PLoS computational biology
|February 21, 2025
概括
准确的诺罗病毒监测需要现在预测模型来纠正报告延迟. 与基本方法相比,GAM和epinowcast等先进模型显著改善了实时案例负担估计.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生监督 公共卫生监督
背景情况:
- 诺罗病毒是导致急性胃肠炎的主要原因,使医疗保健资源受到压力.
- 诊断报告的延迟阻碍了实时的诺罗病毒监测和数据完整性.
研究的目的:
- 开发和评估用于实时诺罗病毒病例负担估计的现在预测模型.
- 评估时间延迟校正和综合征监测对模型准确性的影响.
主要方法:
- 开发并比较了通用添加模型 (GAM),epinowcast模型和贝叶斯结构时间序列 (BSTS) 模型.
- 使用概率评分框架,特别是加权区间评分 (WIS),用于模型评估.
- 将综合症监测数据 (111个在线途径) 纳入BSTS模型.
主要成果:
- 在诺罗病毒病例的现在预测中,GAM和epinowcast模型显著超过了基线启发式方法 (WIS:2.29和3.03对比7.73).
- BSTS模型在变化报告值时显示出可靠性 (WIS: 4.57).
- 综合症监测数据没有提高BSTS模型的性能 (WIS: 10.28),这表明其实用性有限.
结论:
- 考虑到报告延迟的现在预测模型可以提高对诺罗病毒监测数据的理解.
- 模型性能受到报告延迟模式的重大影响.
- 准确的实时决策依赖于由延迟特征告知的强大的建模.
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