Detection of Infectious Disease Outbreaks From Laboratory Data With Reporting Delays
利用存在报告延迟的实验室数据检测传染病暴发AI 解读 ↓
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利用存在报告延迟的实验室数据检测传染病暴发
- 研究问题
- 如何在标本采集与实验室结果报告存在延迟时及时检测传染病暴发，并把延迟分布估计的不确定性纳入检验？
- 方法与思路
- 作者监测当前及过去若干时间单位内确认的标本数量，以历史计数估计无暴发基线，构造检验统计量并推导零假设方差。
- 材料陈述的贡献
- 摘要称，方法显式处理报告延迟并计入延迟分布估计误差；模拟与测试数据应用表明，其可能改善既有临时调整方法的表现。
- 阅读关注点
- 核对监测窗口参数、历史基线与延迟分布的估计方式，特别检查延迟随时间变化、基线污染及误报和检测及时性的权衡。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明，不能推断刊本变化；摘要未说明检验适用条件、阈值选择或性能改善的具体幅度。
原文摘要
Many statistical surveillance systems for the timely detection of outbreaks of infectious disease operate on laboratory data. Such data typically incur reporting delays between the time at which a specimen is collected for diagnostic purposes, and the time at which the results of the laboratory analysis become available. Statistical surveillance systems currently in use usually make some ad hoc adjustment for such delays, or use counts by time of report. We propose a new statistical approach that takes account of the delays explicitly, by monitoring the number of specimens identified in the current and past m time units, where m is a tuning parameter. Values expected in the absence of an outbreak are estimated from counts observed in recent years (typically 5 years). We study the method in the context of an outbreak detection system used in the United Kingdom and several other European countries. We propose a suitable test statistic for the null hypothesis that no outbreak is currently occurring. We derive its null variance, incorporating uncertainty about the estimated delay distribution. Simulations and applications to some test datasets suggest the method works well, and can improve performance over ad hoc methods in current use. Supplementary materials for this article are available online.