title: Bivariate method for spatio-temporal syndromic surveillance.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  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.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA. pagano@hsph.harvard.edu
authors.forename: Al
authors.initials: A
authors.lastname: Ozonoff
authors.forename: L
authors.initials: L
authors.lastname: Forsberg
authors.forename: M
authors.initials: M
authors.lastname: Bonetti
authors.forename: M
authors.initials: M
authors.lastname: Pagano
mesh_terms: D016000:Cluster Analysis; D003710:Demography; D004196:Disease Outbreaks / Q000517:prevention & control; D015991:Epidemiologic Measurements; D006801:Humans; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*