Surveillance Systems

Bio-surveillance systems monitor multiple data streams (over-the-counter (OTC) sales, Emergency Department visits, etc.) to detect both natural disease outbreaks (e.g. influenza) and bio-terrorist attacks (e.g. anthrax re-lease). Many detection algorithms show impressive results under simulated environments, but the complex behavior of real-world data and high costs associated with processing false positives make it difficult to develop practical bio-surveillance systems. We believe that using expert knowledge from public health officials will help us to better understand the real-world data, improving our ability to distinguish actual disease outbreaks from non-outbreak patterns.

Objective

This paper describes the evolution of a bio-surveillance system that incorporates user feedback to improve system utility and usability. The system monitors national-level OTC pharmacy sales on a daily basis. We use fast spatio-temporal scan statistics to detect disease outbreaks.

On August 20th and 21st, 2007, Ohio sustained heavy rains which resulted in severe flooding over a nine-county area in the north-central part of the state. Increased hospital emergency department (ED) visits were expected for gastrointestinal illnesses, but this was not observed. After a media report on September 4, 2007 suggested swarms of mosquitoes were plaguing residents, ED character-specific data were analyzed to see if these data could confirm the report.

Objective

This retrospective analysis of text fragments in emergency department chief complaints illustrates the usefulness of syndromic surveillance in providing timely situational awareness of insect prevalence in post-flood situations.

The global health threat of highly pathogenic avian influenza H5N1 has been increasing rapidly in the world since the crosscountry outbreaks during 2003-04. In South and East Asia, the human influenza A (H3N2) was proved to be seeded there with occurring annual cases. Intensive surveillance of influenza is the most urgent strategy to avoid large-scale epidemics and high case fatality rates. Sentinel physicians’ surveillance is the most sensitive mechanism to reflect the health status of community people. In France and Japan, comprehensive sentinel-physician surveillance systems were set up and geographic information system was applied to display the diffusion patterns of influenza-like illness. Kriging method, which was used to display the diffusion, was hard to monitor the multiple temporal and spatial dimensions in one map. Therefore, Ring maps were proposed to overcome this difficulty.

Objective

This study describes a visualizing ring maps to monitor the alert levels of Influenza-like illness, and provide possible insights of temporal and spatial diffusion patterns in epidemic and nonepidemic seasons.

This abstract describes Missouriís experience with syndromic surveillance. Missouri has expanded from acquiring pre-tabulated data from volunteers to receiving patient-level data via electronic feeds from 85 hospitals across the state processed through multiple analysis, visualization, and reporting tools. Missouri and its partners use these data for early event detection and situational awareness at the state and local levels.

This paper describes a method of avoiding false alerts in automated syndromic surveillance algorithms which monitor the temporal relationship between a particular monitored syndrome (the ìtargetî) in relationship to other reference healthcare data streams (the ìcontextî).

The objective of this project was to classify and extract mental health emergency department (ED) visits from the Houston Real-time Outbreak and Disease Surveillance (RODS) system. In addition, this project will offer a

This paper describes a hybrid (event-based and indicator-based) surveillance platform designed to streamline the collaboration between domain experts and machine learning algorithms for detection, prediction and response to health-related events (such as disease outbreaks).

This paper examines the continued usefulness, through the 2005-06 influenza season, of a hospital admissions-based syndromic surveillance system as a supplement to laboratory and clinical influenza surveillance in preparation for pandemic influenza.

To inform health professionals and the public directly about real-time utilization of local Emergency Departments for respiratory and gastrointestinal illness to enable enhanced communication and collaboration between Public Health and health care workers.

The UNC Department of Emergency Medicine (UNC DEM) conducted an online survey to better understand the surveillance needs of Infection Control Practitioners (ICPs) in North Carolina and solicit feedback on the utility of the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT).