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Bivariate method for spatio-temporal syndromic surveillance
Al Ozonoff1, L Forsberg, M Bonetti
1Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA. pagano@hsph.harvard.edu
Introduction:
Statistical analysis of syndromic data has typically focused on univariate test statistics for spatial, temporal, or spatio-temporal surveillance. However, this approach does not take full advantage of the information available in the data.
Objectives:
A bivariate method is proposed that uses both temporal and spatial data information.
Methods:
Using upper respiratory syndromic data from an eastern Massachusetts health-care provider, this paper illustrates a bivariate method and examines the power of this method to detect simulated clusters.
Results:
Use of the bivariate method increases detection power.
Conclusions:
Syndromic surveillance systems should use all available information, including both spatial and temporal information.
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