Abstract
A simple and an efficient algorithm is proposed for prospective disease surveillance using spatial CUSUSM charts. With this method, spatially correlated Poisson CUSUSM statistics are computed for small neighborhoods and the false discovery rate is controlled using the popular Benjamini-Yekutieli procedure. Simulation studies provide convincing evidence of the strength of the method in rapid identification of disease clusters. The results produced by the method are easily interpretable without a high level of statistical expertise.