Scalable detection of irregular disease clusters using soft compactness constraints

Abstract

Introduction The spatial scan statistic (1) detects significant spatial clusters of disease by maximizing a likelihood ratio statistic F(S) over a large set of spatial regions, typically constrained by shape. The fast localized scan (2) enables scalable detection of irregular clusters by searching over proximity-constrained subsets of locations, using the linear-time subset scanning (LTSS) property to efficiently search over all subsets of each location and its k 1 nearest neighbors. However, for a fixed neighborhood size k, each of the 2 subsets are considered equally likely, and thus the fast localized scan does not take into account the spatial attributes of a subset. Hence, we wish to extend the fast localized scan by incorporating soft constraints, which give preference to spatially compact clusters while still considering all subsets within a given neighborhood.