Spatially Clustered Outbreak Detection Using the EWMA SCAN Statistics with Multiple Sized Windows

摘要

Spatio-temporal surveillance methods for detecting outbreaks of disease are fairly common in the literature with the SCAN statistic setting the benchmark. If the shape and size of the outbreaks are known in advance, then the SCAN statistic can be trained to efficiently detect these, however this is seldom true. Therefore, we want to devise plans that are efficient at detecting a number of outbreaks that vary in size and shape. This article examines plans which use the exponential weighted moving average statistic to build temporal memory into plans and tries to develop robust plans for detecting outbreaks of unknown shapes and sizes.