Numerical simulations showed that VBScan has higher power of detection, sensitivity and positive predicted value than the Elliptic PST. Instead of changing the map, VBScan modifies the metric used to define the distance between cases, without requiring the cartogram construction. The ability to promptly detect space-time clusters of disease outbreaks, when the number of individuals is large, was shown to be feasible, due to the reduced computational load of VBScan. An application for dengue fever in a small Brazilian city is presented. Monte Carlo replications of the original data are used to evaluate the significance of the clusters. Finally, those clusters are evaluated through the scan statistic. The successive removal of edges from the Voronoi distance MST generates sub-trees which are the potential space-time clusters. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance MST linking the cases. The number of Voronoi cells boundaries intercepted by the line segment joining two cases points defines the Voronoi distance between those points. A Voronoi diagram is built for points representing population individuals (cases and controls). ResultsĪ fast method for the detection and inference of point data set space-time disease clusters is presented, the Voronoi Based Scan (VBScan). That method is quite effective, but the cartogram construction is computationally expensive and complicated. The original map is cartogram transformed, such that the control points are spread uniformly. Recently, the concept of Minimum Spanning Tree (MST) was applied to specify the set of potential clusters, through the Density-Equalizing Euclidean MST (DEEMST) method, for the detection of arbitrarily shaped clusters. Usually a window of cylindrical shape is employed, with a circular or elliptical base in the space domain. The Prospective Space-Time scan statistic (PST) is widely used for the evaluation of space-time clusters of point event data.
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