在多站点环境中检测疫情的统计算法:用于病假监测的应用程序
Tom Duchemin1, Angela Noufaily2, Mounia N Hocine1
1Conservatoire National des Arts et Métiers, Paris, France.
Bioinformatics advances
|July 31, 2023
概括
这项研究引入了用于公共卫生监测的改进的偏差检测算法. 这种新方法提高了在多个地点检测疾病爆发的精度,提高了准确性,减少了虚假警报.
科学领域:
- 流行病学和公共卫生监测监测
- 为疫情检测和发现提供统计建模.
- 生物统计学 生物统计学
背景情况:
- 公共卫生监测依赖于算法来检测与健康有关的病例的异常增加,称为异常,以信号潜在的疫情.
- 现有的方法,比如法林顿灵活算法,主要专注于检测偏差*次*,但缺乏特定位置的检测功能.
- 随着流行病学数据的数量和多样性不断增加,以及新出现的流行病威胁,需要更复杂的监控算法来检测时间和空间偏差.
研究的目的:
- 开发用于多站点监控的增强异常检测算法,扩展近似波松回归法林顿灵活算法的功能.
- 主要目标是提高疫情检测的准确性,通过识别不仅是什么时候,而且在哪里发生异常.
- 将病假数据监测纳入跨公司,作为识别公司特定偏差的实际应用.
主要方法:
- 开发一种基于负双项混合效应回归模型的新型算法,将不同站点的随机效应术语纳入其中.
- 引入一种新的重权程序,旨在减轻过去偏差对当前检测灵敏度的影响.
- 在COVID-19大流行期间使用模拟和真实世界病假数据进行实施和验证,在R统计软件 (glmmTMB包) 中执行.
主要成果:
- 模拟表明,与法林顿灵活算法相比,新算法提供了更好的假阳性率,同时保持了超过基线3个标准偏差的疫情的类似检测概率.
- 该算法实现了较高的检测率显著的激增,达到100%的检测,当病例数超过八个基线标准偏差.
- 适用于COVID-19病假数据的应用成功识别了大流行的影响,展示了其在多站点监控场景中的现实世界的适用性和有效性.
结论:
- 拟议的负双项混合效应模型与特定站点的随机效应和重权程序代表了多站点偏差检测的重大进步.
- 该算法提供了增强的性能,特别是在减少虚假阳性和改善在各种数据场景中检测真实爆发时.
- 这种新方法为面对复杂的流行病学挑战的现代公共卫生监测系统提供了有价值的前景和实际实施.
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