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检测美国许多综合征监测系统中的新爆发或聚集性疾病。然而，它无法检测到非圆形、形状不规则的星团。Tango和Takahashi(2005)提出的灵活的空间扫描统计量也被用于检测不规则形状的星团。然而，由于计算量大，该方法对候选簇的搜索设置了最多30个近邻的可行限制。在这项研究中，我们提出了一种灵活的空间扫描统计量，它使用Tango(2008)提出的受限似然比来实现，以(1)消除30个近邻的限制，(2)比原始的灵活空间扫描统计量具有惊人的更少的计算时间。作为一个副作用，通过蒙特卡罗模拟表明，当星团的相对风险变大时，它能够合理地检测出任何形状的星团。

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.