Int J Health Geogr - Detection of arbitrarily-shaped clusters using a neighbor-expanding approach: a case study on murine typhus in south Texas.

Tópicos

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Resumo

CKGROUND: Kulldorff's spatial scan statistic has been one of the most widely used statistical methods for automatic detection of clusters in spatial data. One limitation of this method lies in the fact that it has to rely on scan windows with predefined shapes in the search process, and therefore it cannot detect cluster with arbitrary shapes. We employ a new neighbor-expanding approach and introduce two new algorithms to detect cluster with arbitrary shapes in spatial data. These two algorithms are called the maximum-likelihood-first (MLF) algorithm and non-greedy growth (NGG) algorithm. We then compare the performance of these two new algorithms with the spatial scan statistic (SaTScan), Tango's flexibly shaped spatial scan statistic (FlexScan), and Duczmal's simulated annealing (SA) method using two datasets. Furthermore, we utilize the methods to examine clusters of murine typhus cases in South Texas from 1996 to 2006.RESULT: When compared with the SaTScan and FlexScan method, the two new algorithms were more flexible and sensitive in detecting the clusters with arbitrary shapes in the test datasets. Clusters detected by the MLF algorithm are statistically more significant than those detected by the NGG algorithm. However, the NGG algorithm appears to be more stable when there are no extreme cluster patterns in the data. For the murine typhus data in South Texas, a large portion of the detected clusters were located in coastal counties where environmental conditions and socioeconomic status of some population groups were at a disadvantage when compared with those in other counties with no clusters of murine typhus cases.CONCLUSION: The two new algorithms are effective in detecting the location and boundary of spatial clusters with arbitrary shapes. Additional research is needed to better understand the etiology of the concentration of murine typhus cases in some counties in south Texas.

Resumo Limpo

ckground kulldorff spatial scan statist one wide use statist method automat detect cluster spatial data one limit method lie fact reli scan window predefin shape search process therefor detect cluster arbitrari shape employ new neighborexpand approach introduc two new algorithm detect cluster arbitrari shape spatial data two algorithm call maximumlikelihoodfirst mlf algorithm nongreedi growth ngg algorithm compar perform two new algorithm spatial scan statist satscan tango flexibl shape spatial scan statist flexscan duczmal simul anneal sa method use two dataset furthermor util method examin cluster murin typhus case south texa result compar satscan flexscan method two new algorithm flexibl sensit detect cluster arbitrari shape test dataset cluster detect mlf algorithm statist signific detect ngg algorithm howev ngg algorithm appear stabl extrem cluster pattern data murin typhus data south texa larg portion detect cluster locat coastal counti environment condit socioeconom status popul group disadvantag compar counti cluster murin typhus casesconclus two new algorithm effect detect locat boundari spatial cluster arbitrari shape addit research need better understand etiolog concentr murin typhus case counti south texa

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