The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be ...
Background: Irregularly shape d spatial clusters are difficult to delineate. A cluster found by an a...
The New York City Department of Health and Mental Hygiene has operated an emergency department syndr...
SUMMARY. Many current statistical methods for disease clustering studies are based on a hypothesis t...
Background\ud The task of spatial cluster detection involves finding spatial regions where some prop...
We propose a new Bayesian method for spatial cluster detection, the “Bayesian spatial scan statistic...
AbstractMethods for spatial cluster detection attempt to locate spatial subregions of some larger re...
The detection of areas in which the risk of a particular disease is significantly elevated, leading ...
Background: There is considerable uncertainty in the disease rate estimation for aggregated area map...
This thesis develops a latent modeling framework and likelihood based inference tool to detect multi...
The detection of regions with unusually high risk plays an important role in disease mapping and the...
Esta tese aborda o problema de detecção de clusters espaciais e espaços-temporais. Dois algoritmos p...
The authors were partially supported by the Brazilian institutions CAPES, CNPq and Fapemig.Backgroun...
We propose a novel tool for testing hypotheses concerning the adequacy of environmentally defined f...
Background Cluster detection is an important part of spatial epidemiology because it...
Background Cluster detection is an important part of spatial epidemiology because it can help identi...
Background: Irregularly shape d spatial clusters are difficult to delineate. A cluster found by an a...
The New York City Department of Health and Mental Hygiene has operated an emergency department syndr...
SUMMARY. Many current statistical methods for disease clustering studies are based on a hypothesis t...
Background\ud The task of spatial cluster detection involves finding spatial regions where some prop...
We propose a new Bayesian method for spatial cluster detection, the “Bayesian spatial scan statistic...
AbstractMethods for spatial cluster detection attempt to locate spatial subregions of some larger re...
The detection of areas in which the risk of a particular disease is significantly elevated, leading ...
Background: There is considerable uncertainty in the disease rate estimation for aggregated area map...
This thesis develops a latent modeling framework and likelihood based inference tool to detect multi...
The detection of regions with unusually high risk plays an important role in disease mapping and the...
Esta tese aborda o problema de detecção de clusters espaciais e espaços-temporais. Dois algoritmos p...
The authors were partially supported by the Brazilian institutions CAPES, CNPq and Fapemig.Backgroun...
We propose a novel tool for testing hypotheses concerning the adequacy of environmentally defined f...
Background Cluster detection is an important part of spatial epidemiology because it...
Background Cluster detection is an important part of spatial epidemiology because it can help identi...
Background: Irregularly shape d spatial clusters are difficult to delineate. A cluster found by an a...
The New York City Department of Health and Mental Hygiene has operated an emergency department syndr...
SUMMARY. Many current statistical methods for disease clustering studies are based on a hypothesis t...