Expectation-based scan statistics extend the traditional spatial scan statistic approach by using historical data to infer the expected counts for each spatial location, then detecting regions with higher than expected counts. Here we consider five recently proposed expectation-based statistics: the expectation-based Poisson (EBP), expectation-based Gaussian (EBG), population-based Poisson (PBP), populationbased Gaussian (PBG), and robust Bernoulli-Poisson (RBP) methods. We also consider five different time series analysis methods used to predict the expected counts (including the Holt-Winters method and moving averages optionally adjusted for day of week and seasonality), giving a total of 25 methods to compare. All of these methods are detailed in the full paper.
Objective
We present a systematic empirical comparison of five recently proposed expectation-based scan statistics, in order to determine which methods are most successful for which spatial disease surveillance tasks.