On detecting space-time clusters

Abstract

Detection of space-time clusters is an important function in various domains (e.g., epidemiology and public health). The pioneering work on the spatial scan statistic is often used as the basis to detect and evaluate such clusters. State-of-the-art systems based on this approach detect clusters with restrictive shapes that cannot model growth and shifts in location over time. We extend these methods significantly by using the flexible square pyramid shape to model such effects. A heuristic search method is developed to detect the most likely clusters using a randomized algorithm in combination with geometric shapes processing. The use of Monte Carlo methods in the original scan statistic formulation is continued in our work to address the multiple hypothesis testing issues. Our method is applied to a real data set on brain cancer occurrences over a 19 year period. The cluster detected by our method shows both growth and movement which could not have been modeled with the simpler cylindrical shapes used earlier. Our general framework can be extended quite easily to handle other flexible shapes for the space-time clusters.