title: A CUSUM framework for detection of space-time disease clusters using scan statistics.
abstract: 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.
authors.affiliation: Biostatistics, AstraZeneca R&D, Mölndal SE 43183, Sweden. Christian.Sonesson@astrazeneca.com
authors.forename: Christian
authors.initials: C
authors.lastname: Sonesson
mesh_terms: 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