Abstract
In this paper, we propose a Z-Score Based Multi-level Spatial Clustering (ZMSC) algorithm for the early detection of emerging disease outbreaks. Using semi-synthetic data for algorithm evaluation, we compared ZMSC with the Wavelet Anomaly Detector [1], a temporal algorithm, and two spatial clustering algorithms: Kulldorff’s spatial scan statistic [2] and Bayesian spatial scan statistic [3]. ROC curve analysis shows that ZMSC has better discriminatory ability than the three compared algorithms. ZMSC demonstrated significant computational efficiency—1000x times faster than both spatial algorithms. Finally, ZMSC had the highest cluster positive predictive values of all the algorithms. However, ZMSC showed a 0.5-1 day average delay in detection when the false alarm rate was lower than one false alarm for every five days. We conclude that the ZMSC algorithm improves current methods of spatial cluster detection by offering better discriminatory ability, faster performance and more exact cluster identification.
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Que, J., Tsui, FC., Espino, J. (2008). A Z-Score Based Multi-level Spatial Clustering Algorithm for the Detection of Disease Outbreaks. In: Zeng, D., Chen, H., Rolka, H., Lober, B. (eds) Biosurveillance and Biosecurity . BioSecure 2008. Lecture Notes in Computer Science(), vol 5354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89746-0_11
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DOI: https://doi.org/10.1007/978-3-540-89746-0_11
Publisher Name: Springer, Berlin, Heidelberg
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