ISDS Conference

The H5N1 avian influenza virus is now considered endemic in poultry in some parts of the world and the continued exposure in humans suggests that the risk of the virus evolving into a more transmissible agent in humans − a step towards worldwide pandemic – remains high. Universities, with large assembly of students and student movements determined by the class schedules and travel routes between classes, in addition to the faculty and staff located in close proximity, are extremely susceptible environments to the spread of pandemic events. Moreover, large universities in the U.S. often have a good proportion of international students, who commute to/from their home country within their study period. Therefore, a good surveillance system to detect disease outbreaks is essential to support a system that is robust to this high impact low probability disruptive event.

Objective

This paper describes a framework for an aberration detection method − change-point analysis for mean and variance − adapted for Poisson-distributed data, for syndromic surveillance in an academic environment.

Objective

Several authors have described ways to introduce artificial outbreaks into time series for the purpose of developing, testing, and evaluating the effectiveness and timeliness of anomaly detection algorithms, and more generally, early event detection systems. While the statistical anomaly detection methods take into account baseline characteristics of the time series, these simulated outbreaks are introduced on an ad hoc basis and do not take into account those baseline characteristics. Our objective was to develop statistical-based procedures to introduce artificial anomalies into time series, which thus would have wide applicability for evaluation of anomaly detection algorithms against widely different data streams.

To propose a new spatial scan statistic that has higher ability of pinpointing the true cluster.

This paper describes a methodology for detecting irregular space-time cluster using the space time permutation scan statistic. The methodology includes sequential Monte Carlo simulation and distribution approximation to estimate the error type I.

This paper describes a simple technique for utilizing linked health information in syndromic surveillance. Using knowledge of which patient encounters resulted in laboratory test requests and prescriptions may improve sensitivity and specificity of detection algorithms.

This paper proposes a new scan statistic which detects disease clusters more accurately than that based on the likelihood ratio.

This paper describes a new expectation-based scan statistic that is robust to outliers (individual anomalies at the store level that are not indicative of outbreaks). We apply this method to prospective monitoring of over-the-counter (OTC) drug sales data, and demonstrate that the robust statistic improves timeliness and specificity of outbreak detection.

To evaluate four algorithms with varying baseline periods and adjustment for day of week for anomaly detection in syndromic surveillance data.

The use of spatially-based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health datasets, by transposing their spatial locations through a process informed by the underlying population density. Further, we measure the impact of blurring patient locations on detection of spatial clustering as measured by the SaTScan purely-spatial Bernoulli scanning statistic.

Syndromic surveillance has traditionally been used by public health to supplement mandatory disease reporting. The use of chief complaints as a data source is common for early event detection. Though some public health syndromic surveillance systems allow individual hospitals to view their own data through a web interface, many ICPs have the experience and knowledge-base to conduct their own surveillance and analysis internally. Additionally, they often have interests specific to their hospital which may motivate them to conduct additional syndromic surveillance projects themselves. Lastly, in many cases, ICPs are better able to investigate problems with chief complaint syndrome categorization and aberrations within their own facility before notification of public health staff. A good understanding of the foundation of syndromic surveillance by hospital ICPs can be extremely beneficial when paired with public health to investigate possible cases and outbreaks. ICPs at Greenville Hospital System (GHS), composed of 1110 beds, a level I trauma center with an average of 85,000 visits per year plus three smaller outlying emergency rooms, has had interest in syndromic surveillance for many years and collected data manually for trend analysis using Microsoft Excel to monitor chief complaint data since August 2003.

Objective

Demonstrate the use and benefit to hospital-based infection control practitioners (ICP) of chief complaint data for syndromic surveillance in partnership with public health to assist with traditional public health disease investigations.