Abstract

BACKGROUND:

The spatial scan statistic is a widely used statistical method for the automatic detection of disease clusters from syndromic data. Recent work in the disease surveillance community has proposed many variants of Kulldorff's original spatial scan statistic, including expectation-based Poisson and Gaussian statistics, and incorporates a variety of time series analysis methods to obtain expected counts. We evaluate the detection performance of twelve variants of spatial scan, using synthetic outbreaks injected into four real-world public health datasets.

RESULTS:

The relative performance of methods varies substantially depending on the size of the injected outbreak, the average daily count of the background data, and whether seasonal and day-of-week trends are present. The expectation-based Poisson (EBP) method achieves high performance across a wide range of datasets and outbreak sizes, making it useful in typical detection scenarios where the outbreak characteristics are not known. Kulldorff's statistic outperforms EBP for small outbreaks in datasets with high average daily counts, but has extremely poor detection power for outbreaks affecting more than of the monitored locations. Randomization testing did not improve detection power for the four datasets considered, is computationally expensive, and can lead to high false positive rates.

CONCLUSION:

Our results suggest four main conclusions. First, spatial scan methods should be evaluated for a variety of different datasets and outbreak characteristics, since focusing only on a single scenario may give a misleading picture of which methods perform best. Second, we recommend the use of the expectation-based Poisson statistic rather than the traditional Kulldorff statistic when large outbreaks are of potential interest, or when average daily counts are low. Third, adjusting for seasonal and day-of-week trends can significantly improve performance in datasets where these trends are present. Finally, we recommend discontinuing the use of randomization testing in the spatial scan framework when sufficient historical data is available for empirical calibration of likelihood ratio scores.