Wieland Shannon C, Brownstein John S, Berger Bonnie, Mandl Kenneth D
Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA.
Proc Natl Acad Sci U S A. 2007 May 29;104(22):9404-9. doi: 10.1073/pnas.0609457104. Epub 2007 May 22.
Existing disease cluster detection methods cannot detect clusters of all shapes and sizes or identify highly irregular sets that overestimate the true extent of the cluster. We introduce a graph-theoretical method for detecting arbitrarily shaped clusters based on the Euclidean minimum spanning tree of cartogram-transformed case locations, which overcomes these shortcomings. The method is illustrated by using several clusters, including historical data sets from West Nile virus and inhalational anthrax outbreaks. Sensitivity and accuracy comparisons with the prevailing cluster detection method show that the method performs similarly on approximately circular historical clusters and greatly improves detection for noncircular clusters.
现有的疾病聚集性检测方法无法检测出所有形状和大小的聚集性，也无法识别那些高估聚集性真实范围的高度不规则集合。我们引入了一种基于经变形地图转换后的病例位置的欧几里得最小生成树来检测任意形状聚集性的图论方法，该方法克服了这些缺点。通过使用几个聚集性实例，包括来自西尼罗河病毒和吸入性炭疽疫情的历史数据集，对该方法进行了说明。与主流聚集性检测方法的敏感性和准确性比较表明，该方法在近似圆形的历史聚集性上表现相似，而在非圆形聚集性的检测上有显著改进。
