A new PSO-optimized geometry of spatial and spatio-temporal scan statistics for disease outbreak detection

The spatial and spatio-temporal scan statistics proposed by Kulldorff have been applied to a number of geographical disease cluster detection problems. As the shape of the scanning window used in these methods is circular or elliptic, they cannot find irregularly shaped clusters, say clusters occurring along river valleys or in cases where disease transmission is linked to the road network. In this study, we propose a more flexible geometric structure to be used as a spatial or spatio-temporal scanning window. A particle swarm optimization (PSO) is used to optimize the scanning window to determine disease clusters. We evaluated the proposed method over a number of spatial and spatio-temporal datasets (Breast cancer mortality in Northeastern US 1988-1992 and different types of cancer in New Mexico 1982-2007). Experimental results demonstrate that the introduced approach surpasses the results produced by the circular and elliptic scan statistics in terms of efficiency, especially when dealing with irregularly shaped clusters.