用于暴发检测方法统计评估的开源环境。
An open source environment for the statistical evaluation of outbreak detection methods.
作者信息
Lumley Thomas, Sebestyen Krisztian, Lober William B, Painter Ian
机构信息
Department of Biostatistics, Seattle, Washington, USA.
出版信息
AMIA Annu Symp Proc. 2005;2005:1037.
Abstract
We describe the design and initial steps to implementation of a computational framework for evaluating outbreak detection methods. The framework will include components for combining simulated and historical data to create artificial outbreaks and components that implement various outbreak detection algorithms. The first algorithms to be implemented are the three Cumulative Sums (cusum) methods described in the CDC Early Aberration Reporting System.
摘要
我们描述了一个用于评估疫情检测方法的计算框架的设计及实施的初步步骤。该框架将包括用于组合模拟数据和历史数据以创建人工疫情的组件，以及实施各种疫情检测算法的组件。首先要实施的算法是美国疾病控制与预防中心早期异常报告系统中描述的三种累积和（cusum）方法。
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