Fast detection of arbitrarily shaped disease clusters.
Disease cluster detection and evaluation have commonly used spatial statistics methods that scan the map with a fixed circular window to locate candidate clusters. Recently, there has been interest in searching for clusters with arbitrary shape. The circular scan test retains high power of detecting a cluster, but does not necessarily identify the exact regions contained in a non-circular cluster particularly well. We propose, implement and evaluate a new procedure that is fast and produces clusters estimates of arbitrary shape in a rich class of possible cluster candidates. We showed that our methods contain the so-called upper level set method as a particular case. We present a power study of our method and, among other results, the main conclusion is that the likelihood-based arbitrarily shaped scan method is not appropriate to find a cluster estimate. When the parameter space includes the set of all possible spatial clusters in a map, a large and discrete parameter space, maximum likely cluster estimates tend to overestimate the true cluster by a large extent. This calls for a new approach different from the maximum likelihood method for this important public health problem.
Assunção
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Departamento de Estatística, Universidade Federal de Minas Gerais, 31270-901, Belo Horizonte, MG Brazil. assuncao@est.ufmg.br
Costa
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Tavares
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Ferreira
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D001938:Brazil / Q000453:epidemiology; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks; D006801:Humans; D015994:Incidence; D015233:Models, Statistical