A Comparative Analysis of Multivariate Statistical Detection Methods Applied to Syndromic Surveillance

Biological terrorism is a threat to the security and well-being of the United States. It is critical to detect the presence of these attacks in a timely manner so that emergency services can provide sufficient and effective responses to minimize or contain the damage inflicted. Syndromic surveillance is the process of monitoring public health-related data and applying statistical tests to determine the potential presence of a disease outbreak in the observed system. This research involved a comparative analysis of two multivariate statistical methods: the multivariate cumulative sum (MCUSUM) and the multivariate exponentially weighted moving average (MEWMA), both modified to look only for increases in disease incidence. While neither of these methods is currently in use in a biosurveillance system, they are among the most promising multivariate methods for this application. This analysis was based on a series of simulations using synthetic syndromic surveillance data that mimics various types of background disease incidence and outbreaks. The authors found that, similar to results for the univariate CUSUM and EWMA, the directionally sensitive MCUSUM and MEWMA perform very similarly.