A CUSUM framework for detection of space-time disease clusters using scan statistics.
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.
Sonesson
Christian
C
Biostatistics, AstraZeneca R&D, Mölndal SE 43183, Sweden. Christian.Sonesson@astrazeneca.com
D016000:Cluster Analysis; D021821:Communicable Diseases, Emerging / Q000453:epidemiology*; D006801:Humans; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D013548:Sweden / Q000453:epidemiology; D014406:Tularemia