A cluster is an aggregation of disease in space, in time, or in both space and time.
Cases of a disease can be referenced to a specific location, such as a residence, and time, such as the date of diagnosis. Disease clusters occur when more cases are identified at a particular place and/or time than would otherwise be expected. The study of disease clusters may suggest possible factors and exposures influencing risk for a disease. More likely, cluster identification will provide incentive to undertake a comprehensive epidemiologic study.
Dr. John Snow's study of the 1854 London cholera outbreak is an historic example of a cluster analysis that suggested an effective intervention. In brief, the outbreak of cholera was detected by Dr. Snow even before the bacterium that causes cholera had been identified. He mapped mortality and found that most deaths occurred near the Broad Street Pump. Once the handle of the pump was removed, the outbreak subsided.
Since the time of the London cholera outbreaks, more sophisticated statistical analyses have been developed to detect clustering. Advances in computer databases, Geographic Information Systems, and statistical techniques have augmented our toolbox for the study of disease clusters. Many of the methods offered in ClusterSeer are very new, developed in the last decade.
Cluster statistics offer criteria to determine when observed patterns of disease significantly depart from expected patterns. ClusterSeer includes methods that explore different kinds of clustering: spatial, temporal, and space-time clusters. Many of the methods in ClusterSeer use Monte Carlo randomization techniques to evaluate observed values. These computationally intense methods are more available now that a computer can quickly randomize datasets and perform the calculations.