An extreme value theory approach for the early detection of time clusters. A simulation-based assessment and an illustration to the surveillance of Salmonella
An extreme value theory approach for the early detection of time clusters. A simulation-based assessment and an illustration to the surveillance of Salmonella
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DOI：
10.1002/sim.6275
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发表时间：
2014-12-10
影响因子：
2
通讯作者：
Le Strat, Y.
中科院分区：
文献类型：
--
作者：
Guillou, A.;Kratz, M.;Le Strat, Y.
我们提出了一种新的方法，可以作为预警系统的一部分，用于早期检测适用于公共卫生监测数据的时间簇。该方法基于极值理论。对于报告给监测系统的任何新的特定感染计数，我们将与我们期望能够再次看到这样的水平的时间相对应的重现期相关联。如果达到这样的级别，则会生成警报。虽然标准的EVT只在连续观察的情况下定义，但我们的方法允许处理公共卫生监测框架中发生的离散观察的情况。此外，它不需要对潜在的未知分布函数进行任何假设即可适用。我们的方法的性能是在广泛的模拟研究中评估的，并以法国沙门氏菌监测的真实数据为例进行了说明。版权所有(C)2014 JohnWiley&Sons，Ltd.
We propose a new method that could be part of a warning system for the early detection of time clusters applied to public health surveillance data. This method is based on the extreme value theory (EVT). To any new count of a particular infection reported to a surveillance system, we associate a return period that corresponds to the time that we expect to be able to see again such a level. If such a level is reached, an alarm is generated. Although standard EVT is only defined in the context of continuous observations, our approach allows to handle the case of discrete observations occurring in the public health surveillance framework. Moreover, it applies without any assumption on the underlying unknown distribution function. The performance of our method is assessed on an extensive simulation study and is illustrated on real data from Salmonella surveillance in France. Copyright (C) 2014 JohnWiley & Sons, Ltd.