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Keywords:

  • Surveillance;
  • Aberrations;
  • Clustering;
  • Bootstrap;
  • Scan

Abstract

The detection of unusual patterns in the occurrence of diseases and other health events presents an important challenge to public health surveillance. This paper discusses three analytic methods for identifying aberrations in underlying distributions. The methods are illustrated on selected infectious diseases included in the National Notifiable Diseases Surveillance System of the Centers for Disease Control. Results suggest the utility of such an analytic approach. Further work will determine the sensitivity of such methods to variations in the occurrence of disease. These methods are useful for evaluating and monitoring public health surveillance data.