Significant multiple high-and low-risk regions in event data maps

Abstract

Introduction The Voronoi Based Scan (VBScan) (1) is a fast method for the detection and inference of point data set space-time disease clusters. A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases’ points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance Minimum Spanning Tree (MST) linking the cases. The successive removal of its edges generates subtrees, which are the potential space-time clusters, which are evaluated through the scan statistic. Monte Carlo replications of the original data are used to evaluate cluster significance. In the present work, we modify VBScan to find the best partition dividing the map into multiple lowand high-risk regions.