Spatial Scan Statistics

The Pittsburgh Center of Excellence in Public Health Informatics has developed a probabilistic, decision-theoretic system for disease surveillance and control for use in Allegheny County, PA and later in Tarrant County, TX. This paper describes the software components of the system and its knowledge bases. The paper uses influenza surveillance to illustrate how the software components transform data collected by the healthcare system into population level analyses and decision analyses of potential outbreak-control measures.

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Using the Flow of People in Cluster Detection and Inference

Ferreira

Oliveira

Tavares

et al. 2013

Online Journal of Public Health Informatics

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ObjectiveWe present a new approach to the circular scan method [1] that uses the flow of people to detect and infer clusters of regions with high incidence of some event randomly distributed in a map. We use a real database of homicides cases in Minas Gerais state, in southeast Brazil to compare our proposed method with the original circular scan method in a study of simulated clusters and the real situation. IntroductionThe traditional SaTScan algorithm[1],[2] uses the euclidean distance between centroids of the regions in a map to assemble a connected (in the sense that two connected regions share a physical border) sets of regions. According to the value of the respective logarithm of the likelihood ratio (LLR) a connected set of regions can be classified as a statistically significant detected cluster. Considering the study of events like contagious diseases or homicides we consider using the flow of people between two regions in order to build up a set of regions (zone) with high incidence of cases of the event.In this sense the regions will be closer as the greater the flow of people between them. In a cluster of regions formed according to the criterion of proximity due to the flow of people, the regions will be not necessarily connected to each other.

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Spatial Scan Statistics

Abstract: This article reviews the spatial scan statistic, including its definition, adjustment for overdispersion, bootstrap methods for p ‐value computation, and selected software for implementation.

Cited by 4 publications

References 23 publications

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Probabilistic, Decision-theoretic Disease Surveillance and Control

Using the Flow of People in Cluster Detection and Inference

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