Abstract
Multiple data sources are essential to provide more reliable information regarding the emergence of potential health threats, compared to single source methods. However, only ad hoc procedures have been devised to address the problem of locating, among the many potential solutions, which is the most likely cluster, and determining its significance. We incorporate information from multiple data streams of disease surveillance to achieve more coherent spatial cluster detection by using statistical tools from multi-criteria analysis. Our approach defines in an optimal way, how spatial disease clusters found by the spatial scan statistic can be interpreted in terms of their significance.