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A CUSUM framework for detection of space–time disease clusters using scan statistics
Author(s) -
Sonesson Christian
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2898
Subject(s) - cusum , scan statistic , computer science , statistic , cluster (spacecraft) , statistics , disease surveillance , statistical process control , control chart , data mining , focus (optics) , process (computing) , disease , mathematics , medicine , pathology , programming language , operating system , physics , optics
Several methods for timely detection of emerging clusters of diseases have recently been proposed. We focus our attention on one of the most popular types of method; a scan statistic. Different ways of constructing space–time scan statistics based on surveillance theory are presented. We bridge the ideas from space–time disease surveillance, public health surveillance and industrial quality control and show that previously suggested space–time scan statistics methods can be fitted into a general CUSUM framework. Crucial differences between the methods studied are due to different assumptions about the spatial process. An example is the specification of the spatial regions of interest for a possible cluster, another is the increased rate to be detected within a cluster. We evaluate the detection ability of the methods considering the possibility of a cluster emerging at any time during the surveillance period. The methods are applied to the detection of an increased incidence of Tularemia in Sweden. Copyright © 2007 John Wiley & Sons, Ltd.