AbstractIn this work, Spatio-Temporal Data Mining of disease surveillance data is done, to describe the underlying patterns in disease occurrences across populations and to identify possible causes that could explain them; for better disease core prediction, detection and management. MiSTIC algorithm is used to determine spatial spread of disease core regions (scale of disease prevalence), and the frequency & regularity of occurrence of different locations in space as disease cores. The results show good correlation between the etiologic factors of Salmonellosis and the detected core locations, in addition to the significant observation of highly localized nature of disease prevalence.
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