A Space–Time Scan Statistic for Detecting Emerging Outbreaks
A Space–Time Scan Statistic for Detecting Emerging Outbreaks
复制标题
用于检测新爆发疫情的时空扫描统计数据
DOI：
10.1111/j.1541-0420.2010.01412.x
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发表时间：
2011
期刊：
影响因子：
1.9
通讯作者：
K. Kohriyama
中科院分区：
文献类型：
--
作者：
T. Tango;Kunihiko Takahashi;K. Kohriyama
作为爆发检测的主要分析方法，Kulldorff的时空扫描统计（2001，Journal of the皇家统计学会杂志，A系列164，61-72）已经在许多综合征监测系统中实施。 然而，由于它是基于空间中的圆形窗口，它很难正确地检测实际的非圆形集群。Takahashi等人（2008，International Journal of Health Geographics 7，14）提出了一种具有检测非圆形区域能力的灵活时空扫描统计量。 然而，在我们看来，在这些时空扫描统计数据中定义的最有可能的集群的检测与局部新出现的疾病暴发的检测并不相同，因为前者将观察到的病例数与有条件的预期病例数进行比较。 在这篇文章中，我们提出了一种新的时空扫描统计量，它将观察到的病例数与无条件的预期病例数进行比较，考虑泊松均值的时-时变化，并实现了一个爆发模型，以更及时，更正确地捕捉局部新出现的疾病爆发。 所提出的模型说明了每周监测的数据在北九州市，日本，2006年小学的缺席人数。
Summary As a major analytical method for outbreak detection, Kulldorff's space–time scan statistic (2001, Journal of the Royal Statistical Society, Series A 164, 61–72) has been implemented in many syndromic surveillance systems. Since, however, it is based on circular windows in space, it has difficulty correctly detecting actual noncircular clusters. Takahashi et al. (2008, International Journal of Health Geographics 7, 14) proposed a flexible space–time scan statistic with the capability of detecting noncircular areas. It seems to us, however, that the detection of the most likely cluster defined in these space–time scan statistics is not the same as the detection of localized emerging disease outbreaks because the former compares the observed number of cases with the conditional expected number of cases. In this article, we propose a new space–time scan statistic which compares the observed number of cases with the unconditional expected number of cases, takes a time‐to‐time variation of Poisson mean into account, and implements an outbreak model to capture localized emerging disease outbreaks more timely and correctly. The proposed models are illustrated with data from weekly surveillance of the number of absentees in primary schools in Kitakyushu‐shi, Japan, 2006.