Kulldorff’s spatial scan statistic1 detects significant spatial clusters of disease by maximizing a likelihood ratio statistic over circular spatial regions. The fast localized subset scan2 enables scalable detection of proximity-constrained subsets and increases power to detect irregularly-shaped clusters, However, unconstrained subset scanning within each circular neighborhood2, may not necessarily capture the pattern of interest, and is too under-constrained for use with case/control point data. Thus we propose the star-shaped scan statistic (StarScan), a novel method that efficiently maximizes the loglikelihood ratio over irregularly-shaped clusters, while incorporating soft constraints on smoothness. More precisely, we allow the radius of the cluster around a center location to vary along with angle, and penalize proportional to the total change in radius.
Objective
We present StarScan, a novel scan statistic for accurately detecting irregularly-shaped disease outbreaks. StarScan maximizes a penalized log-likelihood ratio statistic, allowing the radius around a central location to vary as a function of the angle and applying a penalty proportional to the total change in radius.