A Comparison of MCUSUM-based and MEWMA-based Spatiotemporal Surveillance Under Non-homogeneous Populations

Motivated by the application in health‐care surveillance under non‐homogeneous populations, this paper compares the performance of multivariate exponentially weighted moving average (MEWMA)‐based methods with that of multivariate cumulative sum (MCUSUM)‐based methods for spatiotemporal chronic disease surveillance. We perform the comprehensive simulation studies of the MEWMA‐based methods for the selection of weight parameters. Under temporally or spatially non‐homogeneous population trends, we compare the MEWMA methods with the MCUSUM methods, which are specific forms of the spatiotemporal scan statistics if the baseline rate is known. The performance of the MCUSUM methods has extreme variations depending on whether the change occurs on a small population or a large population. When the change occurs in the time period or within the spatial region with a small population, the weighted likelihood ratio‐based MCUSUM has better detection speed, but worse identification of detection clusters than the likelihood ratio‐based MCUSUM. On the other hand, when the change occurs in the time period or within the region with a large population, the weighted likelihood ratio‐based MCUSUM has worse detection speed, but better identification than the likelihood ratio‐based MCUSUM. Unlike the MCUSUM methods, the MEWMA‐based methods show relatively stable and robust performance in terms of detection speed, and they show better identification than the MCUSUM‐based methods under most cases.