In disease surveillance, an outbreak is often present in more than one data type. If each data type is analyzed separately rather than combined, the statistical power to detect an outbreak may suffer because no single data source captures all the individuals in the outbreak. Researchers, thus, started to take multivariate approaches to syndromic surveillance. The data sources often analyzed include emergency department data, categorized by chief complaint; over-thecounter pharmaceutical sales data collected by the National Retail Data Monitor (NRDM), and some other syndromic data.
Objective
This study proposes a simulation model to generate the daily counts of over-the-counter medication sales, such as thermometer sales from all ZIP code areas in a study region that include the areas without retail stores based on the daily sales collected from the ZIP codes with retail stores through the NRDM. This simulation allows us to apply NRDM data in addition to other data sources in a multivariate analysis in order to rapidly detect outbreaks.