Outbreak detection algorithms for syndromic surveillance data are becoming increasingly complex. Initial algorithms focused on temporal data but newer methods incorporate geospatial dimensions. As methods evolve, it is important to understand the effects on detection of both algorithm parameters and population characteristics. Intensive, iterative data analyses are required to accomplish this. Even with leading-edge computer hardware, it can take weeks or months to complete analyses using advanced signal detection techniques such as the space-time scan statistic in the SaTScan program.
Given the strategic significance and national security implications of timely and accurate detection, proper tools for studying and thus improving increasingly complex surveillance algorithms are warranted.
Objective
We describe a method to perform computationally intensive analyses on large volumes of syndromic surveillance data using open-source grid computing technology.