Rank-based spatial clustering: an algorithm for rapid outbreak detection.
Rank-based spatial clustering: an algorithm for rapid outbreak detection.
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基于排名的空间聚类：一种快速爆发检测的算法。
DOI：
10.1136/amiajnl-2011-000137
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Tsui,Fu-Chiang
中科院分区：
文献类型：
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作者：
Que,Jialan;Tsui,Fu-Chiang
目的公共卫生监测需要具有足够计算效率的疫情检测算法来处理不断增加的疾病监测数据量。针对这一需求，作者提出了一种空间聚类算法——基于秩的空间聚类（RSC），它可以快速检测传染性但非传染性疾病的爆发。作者将RSC的爆发检测性能与三种成熟的算法（小波异常检测器（WAD）、空间扫描统计（KSS）和贝叶斯空间扫描统计（BSS））进行了比较，这些算法使用真实的疾病监测数据，并将模拟的疾病爆发叠加在一起。测量方法测量以下爆发检测性能指标：接收者工作特征曲线、活动监测工作曲线曲线、聚类阳性预测值、聚类灵敏度和算法运行时间。结果rsc计算效率高。它在检测及时性和爆发定位方面优于其他两种空间算法。在低虚警率下，RSC总体上比时间序列算法WAD具有更好的时效性。结论rsc算法在其他空间算法难以应用的情况下，是一种比较理想的大数据集分析算法。它还允许公共卫生从业人员及时进行调查，提供早期发现和疫情聚集的良好定位。
ObjectivePublic health surveillance requires outbreak detection algorithms with computational efficiency sufficient to handle the increasing volume of disease surveillance data. In response to this need, the authors propose a spatial clustering algorithm, rank-based spatial clustering (RSC), that detects rapidly infectious but non-contagious disease outbreaks.DesignThe authors compared the outbreak-detection performance of RSC with that of three well established algorithms—the wavelet anomaly detector (WAD), the spatial scan statistic (KSS), and the Bayesian spatial scan statistic (BSS)—using real disease surveillance data on to which they superimposed simulated disease outbreaks.MeasurementsThe following outbreak-detection performance metrics were measured: receiver operating characteristic curve, activity monitoring operating curve curve, cluster positive predictive value, cluster sensitivity, and algorithm run time.ResultsRSC was computationally efficient. It outperformed the other two spatial algorithms in terms of detection timeliness, and outbreak localization. RSC also had overall better timeliness than the time-series algorithm WAD at low false alarm rates.ConclusionRSC is an ideal algorithm for analyzing large datasets when the application of other spatial algorithms is not practical. It also allows timely investigation for public health practitioners by providing early detection and well-localized outbreak clusters.