Outbreak Detection Of Spatio-Temporally Smoothed Crashes

Abstract

Spatio-temporal surveillance methods for detecting outbreaks are common with the SCAN statistic setting the benchmark. If the shape and size of the outbreaks are known, then the SCAN statistic can be trained to efficiently detect these, however this is seldom the case. Therefore devising a plan that is efficient at detecting a range of outbreaks that vary in size and shape is important in practical applications. So this paper introduces a method called EWMA Surveillance Trees that uses a binary recursive partitioning approach to locate and detect outbreaks. This approach is explained and then its performance is compared to that of the SCAN statistic in a series of simulation studies. While the SCAN statistic is shown to remain the most effective at detecting outbreaks of a known shape and size, the EWMA Surveillance Trees are shown to be more robust. The method is also applied to an example of actual data from motor vehicle crashes in an area of Sydney Australia from 2000 to 2004 in order to detect dates and geographic regions with outbreaks of crashes above the expected.