Maximum linkage space-time permutation scan statistics for disease outbreak detection.
Maximum linkage space-time permutation scan statistics for disease outbreak detection.
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DOI：
10.1186/1476-072x-13-20
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发表时间：
2014-06-10
影响因子：
4.9
通讯作者：
Kulldorff M
中科院分区：
文献类型：
--
作者：
Costa MA;Kulldorff M
在疾病监测中，前瞻性时空排列扫描统计量通常用于疾病暴发的早期检测。定义潜在疾病簇的扫描窗口是圆柱形的，这不允许将诸如关于道路、景观等的信息等有助于疾病传播的潜在因素合并到簇形状中。此外，柱面扫描窗口假定集群的空间范围不随时间改变。或者，动态时空簇可以指示疾病在时间上的潜在传播。例如，聚集性可能会随着时间的推移而减少，这表明疾病的传播正在消失。提出了两种形状不规则的空时排列扫描统计量。使用图形结构动态地创建集群几何图形。可以创建该图以包括最近邻结构、地理邻接信息或关于监视下的事件的传染行为的任何相关先验信息。新方法以新英格兰三个州的流感病例为例进行了说明，并与圆柱形版本进行了比较。对所提出的任意簇检测技术的一些性质进行了仿真研究。我们已经成功地发展了两种新的时空置换扫描统计方法，它们形状不规则，计算性能也得到了提高。结果表明，这些方法具有快速检测不规则几何形状的疾病爆发的潜力。未来的工作目标是进行密集的模拟研究，以使用不同的场景、案例数量和图形结构来评估所提出的方法。
In disease surveillance, the prospective space-time permutation scan statistic is commonly used for the early detection of disease outbreaks. The scanning window that defines potential clusters of diseases is cylindrical in shape, which does not allow incorporating into the cluster shape potential factors that can contribute to the spread of the disease, such as information about roads, landscape, among others. Furthermore, the cylinder scanning window assumes that the spatial extent of the cluster does not change in time. Alternatively, a dynamic space-time cluster may indicate the potential spread of the disease through time. For instance, the cluster may decrease over time indicating that the spread of the disease is vanishing. This paper proposes two irregularly shaped space-time permutation scan statistics. The cluster geometry is dynamically created using a graph structure. The graph can be created to include nearest-neighbor structures, geographical adjacency information or any relevant prior information regarding the contagious behavior of the event under surveillance. The new methods are illustrated using influenza cases in three New England states, and compared with the cylindrical version. A simulation study is provided to investigate some properties of the proposed arbitrary cluster detection techniques. We have successfully developed two new space-time permutation scan statistics methods with irregular shapes and improved computational performance. The results demonstrate the potential of these methods to quickly detect disease outbreaks with irregular geometries. Future work aims at performing intensive simulation studies to evaluate the proposed methods using different scenarios, number of cases, and graph structures.