Abstract
Spatial scan statistic methods are commonly used for disease surveillance and cluster detection. The isotonic spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. We propose a spatial scan statistic for a multilevel risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the isotonic method and multilevel risk cluster method were evaluated through an intensive simulation study and a real hand foot mouth disease data in a certain city of China in May 2009. Our proposed multilevel method showed more robust and geographical precision with multilevel risk cluster scenarios, especially for a noncentralized high-risk kernel.
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