Statistical Models of Bio-surveillance for Monitoring Disease Outbreak

监测疾病爆发的生物监测统计模型

基本信息

批准号：
16300091
负责人：
TANGO Toshiro
金额：
$ 9.41万
依托单位：
National Institute of Public Health
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2006
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16300091/
关键词：
Disease clustering Likelihood ratio test Poisson distribution Spatial epidemiology Spatial statistics Spatial scan statistic ボアッソン分布

项目摘要

The anthrax terrorist attacks in 2001, the severe acute respiratory syndrome (SARS) outbreak, and a fear of pandemic flu have motivated many public health departments to develop early disease outbreak detection systems in the United States. Early detection of disease outbreaks enables public health officials to implement disease control and prevention measures at the earliest possible time. Particularly in light of the perceived threat of bioterrorism, there has been a spate of recent interest in the development of biosurveillance systems that can detect changes in spatial patterns of disease. In this research, we developed four statistical models/methods that can be implemented in the surveillance system. We first proposed a flexibly shaped spatial scan statistic that can detect irregular shaped clusters within relatively small neighborhoods of each region. The performance of the proposed spatial scan statistic is compared to that of Kulldorff's circular spatial scan statistic with Mo … More nte Carlo simulation by considering several circular and noncircular hot-spot cluster models. The proposed spatial scan statistic is shown to have good usual powers plus the ability to detect the noncircular hot-spot clusters more accurately than the circular one. We second we proposed an extended power of the cluster detection tests, which includes the usual power as a special case. Further, we define the profile of the extended power, which can be expected to play an important role in the evaluation and comparison of several cluster detection tests. The proposed extended power and its profile are demonstrated by two tests -- Kulldorff's circular spatial scan statistic (1995,1997) and a flexible spatial scan statistic proposed by Tango and Takahashi(2005). Third, we propose a flexibly shaped space-time scan statistic based on the flexible spatial scan statistic. In particular, we develop a prospective space-time scan statistic for early detection of disease outbreaks. The performance of the proposed space-time scan statistic is compared with that of the cylindrical scan statistic, using the benchmark data provided by Kulldorff et al. In order to compare the performance of space-time scan statistics we propose a space-time power distribution by extending the purely spatial bivariate power distribution. Daily syndromic surveillance data in Massachusetts, USA, are used to illustrate the proposed test statistic and also to compare the performance of the flexible space-time scan statistic with that of the cylindrical one. Finally, a class of tests with quadratic forms for detecting spatial clustering of health events based on case-control point data was proposed. It includes Cuzick and Edwards' test statistic(1990). Although they used the property of asymptotic normality of the test statistic, we showed that such an approximation is generally poor for moderately large sample sizes. Instead, we suggested a central chi-square distribution as a better approximation to the asymptotic distribution of the test statistic. Furthermore, not only to estimate the optimal value of the unknown parameter on the scale of cluster but also to adjust for multiple testing due to repeating the procedure by changing the parameter value, we proposed the minimum of the profile p-value of the test statistic for the parameter as an integrated test statistic. Less

炭疽恐怖袭击于2001年，严重的急性呼吸道综合征（SARS）爆发以及对大流行流感的恐惧激励了许多公共卫生部门在美国发展了早期疾病爆发检测系统。疾病暴发的早期发现使公共卫生官员能够尽早实施疾病控制和预防措施。特别是考虑到生物恐怖主义的威胁，最近对生物监护系统的发展产生了兴趣，这些系统可以检测到疾病空间模式的变化。在这项研究中，我们开发了可以在监视系统中实施的四种统计模型/方法。我们首先提出了一个柔软形状的空间扫描统计量，该统计量可以检测到每个区域相对较小的社区内的不规则形状簇。通过考虑几个圆形和非圆形热点簇模型，将提出的空间扫描统计量的性能与Kulldorff的圆形空间扫描统计量进行了比较。所提出的空间扫描统计量显示具有良好的常规功能，并且能够比圆形扫描量更准确地检测非圆形热点簇。第二，我们提出了集群检测测试的扩展功率，其中包括通常的功率作为特殊情况。此外，我们定义了扩展功率的曲线，可以预期，在评估和比较几个群集检测测试中，它们可以发挥重要作用。拟议的扩展功率及其轮廓通过两项测试证明：Kulldorff的圆形空间扫描统计（1995,1997）以及Tango和Takahashi（2005）提出的灵活的空间扫描统计量。第三，我们提出了一个基于灵活的空间扫描统计量的灵活形状的时空扫描统计量。特别是，我们开发了一种前瞻性时空扫描统计量，用于早期发现疾病暴发。使用Kulldorff等人提供的基准数据，将提出的时空扫描统计数据与圆柱扫描统计数据进行了比较。为了比较时空扫描统计的性能，我们通过扩展纯粹的空间双变量电源分布来提出一个时空电源分布。美国马萨诸塞州的每日综合症监测数据用于说明拟议的测试统计数据，并将灵活的时空扫描统计数据与圆柱形统计数据进行比较。最后，提出了一类具有二次形式的测试，以基于病例对照点数据检测健康事件的空间聚类。它包括Cuzick和Edwards的测试统计数据（1990）。尽管他们使用了测试统计的不对称正态性的特性，但我们表明，对于中等大型样本量，这种近似值通常很差。取而代之的是，我们建议将中央卡方分布作为对测试统计量的不对称分布的更好近似。此外，不仅要估算群集尺度上未知参数的最佳值，而且还要通过更改参数值重复该过程而调整多重测试，我们提出了参数的测试统计数据的最小值，该参数的最小值作为集成测试统计量。较少的