Bivariate method for spatio-temporal syndromic surveillance.

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Al Ozonoff, L Forsberg, M Bonetti, M Pagano

Author Information

Al Ozonoff: Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA. pagano@hsph.harvard.edu

PMID: 15714631

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.

R01AI28076/NIAID NIH HHS
R01LM007677/NLM NIH HHS

Cluster Analysis

Demography

Disease Outbreaks

Epidemiologic Measurements

Humans

Models, Statistical

Population Surveillance

Journal Article Research Support, U.S. Gov't, P.H.S.

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