Statistical models for detecting emerging outbreaks for health risk monitoring

Kulldorff（1997）提出的圆形空间扫描统计量已被用于在美国的许多综合征监测系统中检测新出现的疫情或聚集。然而，它不能检测非圆形、不规则形状的簇。Tango和Takahashi（2005）提出的柔性空间扫描统计量也被用于检测不规则形状的簇。然而，由于计算量大，该方法对候选聚类的搜索设置了最多30个最近邻的可行限制。在本研究中，我们展示了一种灵活的空间扫描统计量，该统计量采用Tango（2008）提出的限制性似然比来实现：(1)消除了30个近邻的限制；(2)与原始灵活的空间扫描统计量相比，计算时间惊人地少得多。作为一个副作用，它被证明能够检测集群与任何形状相当好，因为集群的相对风险变得大通过蒙特卡罗模拟。

The circular spatial scan statistic proposed by Kulldorff (1997) has been utilized to detect emerging outbreaks or clusters in many syndromic surveillance systems in USA. However, it cannot detect noncircular, irregularly shaped clusters. The flexible spatial scan statistic proposed by Tango and Takahashi (2005) has also been used for detecting irregularly shaped clusters. However, this method sets a feasible limitation of a maximum of 30 nearest neighbors for searching candidate clusters because of heavy computational load. In this study, we show a flexible spatial scan statistic implemented with a restricted likelihood ratio proposed by Tango (2008) to (1) eliminate the limitation of 30 nearest neighbors and (2) to have surprisingly much less computational time than the original flexible spatial scan statistic. As a side effect, it is shown to be able to detect clusters with any shape reasonably well as the relative risk of the cluster becomes large via Monte Carlo simulation.