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Rank-based spatial clustering: an algorithm for rapid outbreak detection
Authors:Jialan Que  Fu-Chiang Tsui
Institution:RODS Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Abstract:

Objective

Public 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.

Design

The 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.

Measurements

The 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.

Results

RSC 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.

Conclusion

RSC 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.
Keywords:Biosurveillance  disease outbreak  spatial clustering  time series  birthday  editorial Office  Health data standards  vocabulary  ontology  Scientific information and Health data policy  Consumer health/patient education information  Information retrieval  NLP  Public health informatics  clinical trials
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