Spatial scan statistics in loglinear models

Authors:
Tonglin Zhang;Ge Lin
Affiliations:
Department of Statistics, Purdue University, West Lafayette, IN, USA;Department of Health Services Research and Administration, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA
Venue:
Computational Statistics & Data Analysis
Year:
2009

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Abstract

The likelihood ratio spatial scan statistic has been widely used in spatial disease surveillance and spatial cluster detection applications. In order to better understand cluster mechanisms, an equivalent model-based approach is proposed to the spatial scan statistic that unifies currently loosely coupled methods for including ecological covariates in the spatial scan test. In addition, the utility of the model-based approach with a Wald-based scan statistic is demonstrated to account for overdispersion and heterogeneity in background rates. Simulation and case studies show that both the likelihood ratio-based and Wald-based scan statistics are comparable with the original spatial scan statistic.