Rank-based spatial clustering: an algorithm for rapid outbreak detection.

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.