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Scalable Detection of Irregular Disease Clusters Using Soft Compactness Constraints

Description

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[k] 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.

Objective

We present a new method for efficiently and accurately detecting irregularly-shaped outbreaks by incorporating "soft" constraints, rewarding spatial compactness and penalizing sparse regions.

Submitted by elamb on