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Development of timely validation and response procedures in the event of cluster detection is also required.
Another approach for cluster detection is a Bayesian method using Markov chain Monte Carlo (MCMC) sampling [ 15, 16, 34, 35].
A well-known early method for spatial cluster detection is the spatial scan statistic developed by Kulldorff [ 3].
An advantage of using the space-time scan statistic for cluster detection is that this method does not require the specification of baseline risk levels.
Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance [ 1], and analysis of fMRI images [ 2].
Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images.
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Network cluster detection was performed based on CFinder 2.0.6, and DAVID was applied to analyse the GO enrichment of gene functions (https://david.ncifcrf.gov/).ncifcrf.gov/
The impact of sampling bias, introduced through non-specialist volunteer collected data, on spatial cluster detection was simulated.
Cluster detection was carried out using the SaTScan™ software package [22].
Cluster detection was based on the first patient isolate of each species-group over the 8-year period.
Gene cluster detection was repeated with the Exophiala dermatitidis and Baudoinia compniacensis TD loci as queries in order to more accurately evaluate homology among these genomes.
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