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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.

Referenced File
Submitted by hparton on

Many cities in the US and the Center for Disease Control and Prevention have deployed biosurveillance systems to monitor regional health status. Biosurveillance systems rely on algorithms that analyze data in temporal domain (e.g., CuSUM) and/or spatial domain (e.g., SaTScan). Spatial domain-based algorithms often require population information to normalize the counts (e.g., emergency department visits) within a geographic region. This paper presents a new algorithm Ellipse-based Clustering Analysis (ECA) that analyzes data in both temporal and spatial domains--using time series analysis for each of zip codes with abnormal counts and using pattern recognition methods for spatial clusters.

 

Objective

This paper describes a new clustering algorithm ECA, which uses a time series algorithm to identify zip codes with abnormal counts, and uses a pattern recognition method to identify spatial clusters in ellipse shapes. Using ellipses could help detect elongated clusters resulting from wind dispersion of bio-agents. We applied the ECA to over-the-counter medicine sales. The pilot study demonstrated the potential use of the algorithm in detection of clustered outbreak regions that could be associated with aerosol release of bio-agents.

Referenced File
Submitted by elamb on

Population surges or large events may cause shift of data collected by biosurveillance systems [1]. For example, the Cherry Blossom Festival brings hundreds of thousands of people to DC every year, which results in simultaneous elevations in multiple data streams (Fig. 1). In this paper, we propose an MGD model to accommodate the needs of dealing with baseline shifts.

Objective:

Outbreak detection algorithms monitoring only disease-relevant data streams may be prone to false alarms due to baseline shifts. In this paper, we propose a Multinomial-Generalized-Dirichlet (MGD) model to adjust for baseline shifts.

 

Submitted by Magou on
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