A multi-level spatial clustering algorithm for detection of disease outbreaks.
In this paper, we proposed a Multi-level Spatial Clustering (MSC) algorithm for rapid detection of emerging disease outbreaks prospectively. We used the semi-synthetic data for algorithm evaluation. We applied BARD algorithm [1] to generate outbreak counts for simulation of aerosol release of Anthrax. We compared MSC with two spatial clustering algorithms: Kulldorff's spatial scan statistic [2] and Bayesian spatial scan statistic [3]. The evaluation results showed that the areas under ROC had no significant difference among the three algorithms, so did the areas under AMOC. MSC demonstrated significant computational efficiency (100 + times faster) and higher PPV. However, MSC showed 2-6 hours delay on average for outbreak detection when the false alarm rate was lower than 1 false alarm per 4 weeks. We concluded that the MSC algorithm is computationally efficient and it is able to provide more precise and compact clusters in a timely manner while keeping high detection accuracy (cluster sensitivity) and low false alarm rates.
Que
Jialan
J
RODS Laboratory, Department of Biomedical Informatics,University of Pittsburgh, USA.
Tsui
Fu-Chiang
FC
D000465:Algorithms; D016000:Cluster Analysis; D003141:Communicable Diseases / Q000175:diagnosis* / Q000453:epidemiology*; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D015994:Incidence; D011159:Population Surveillance / Q000379:methods*; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors