A bivariate method for spatio-temporal syndromic surveillance

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. Here, a bivariate method is proposed that uses both temporal and spatial data information. 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. Use of the bivariate method is shown to increase detection power. In conclusion, syndromic surveillance systems should use all available information, including both spatial and temporal information.