A genetic algorithm for irregularly shaped spatial scan statistics

A new approach is presented for the detection and inference of irregularly shaped spatial clusters, using a genetic algorithm. Given a map divided into regions with corresponding populations at risk and cases, the graph-related operations are minimized by means of a fast offspring generation and efficient evaluation of Kuldorff's spatial scan statistic. A penalty function based on the geometric non-compactness concept is employed to avoid excessive irregularity of cluster geometric shape. The algorithm is an order of magnitude faster and exhibits less variance compared to the simulated annealing scan, and is more flexible than the elliptic scan. It has about the same power of detection as the simulated annealing scan for mildly irregular clusters and is superior for the very irregular ones. An application to breast cancer clusters in Brazil is discussed.