从噪音流行病数据及时干预的不对称限制.
Kris V Parag1,2, Ben Lambert3,4, Christl A Donnelly3,4
1MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
Communications physics
|November 24, 2025
概括
流行病监测数据噪声,由于报告不足和延迟,阻碍了在增长期间及时采取干预决策,但在下降期间有助于放松. 这种不对称性表明主动干预往往是必要的.
科学领域:
- 流行病学 流行病学
- 数学建模的数学建模
- 公共卫生决策 - - 公共卫生决策
背景情况:
- 新出现的传染病管理需要及时的干预,监测数据的不确定性使其复杂化.
- 流行病学数据中的噪音,包括病例报告不足和确定延迟,对有效的决策构成重大挑战.
- 在公共卫生响应中,平衡虚假警报与延迟行动的成本至关重要.
研究的目的:
- 量化监控噪声对干预决策及时性和可靠性的影响.
- 分析启动和放松干预措施之间的决策支持中的不对称性.
- 评估噪音对流行病学参数的影响,如生殖数量和生长率.
主要方法:
- 模拟干预决策作为二元选择,由报告的病例数据和传染性估计提供信息.
- 使用基于值的决策触发因素 (病例数,信心水平) 通过成本效益分析确定.
- 评估了病例报告不足和确定延迟对决定及时性和参数估计对不断增长和衰退的流行病的影响.
主要成果:
- 监控噪声源 (报告不足,延迟) 在流行病增长期间引入干预措施的额外延迟.
- 这些噪音源还会导致疫情扩散期间估计的繁殖数量和增长率的信心减少.
- 在疫情下降期间,噪音源对病例数据产生抵消作用,对传染性估计的累积影响有限.
结论:
- 标准的监测数据为启动干预措施提供了较弱的支持,而不是放松干预措施,在流行病增长期间造成了信息瓶.
- 这种不对称性甚至在先进的反控制算法中也存在.
- 这些发现可能会证明在流行病的早期,增长阶段采取更积极的干预策略是合理的.
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