ISDS Conference

To compare regression models with the modified C2 algorithm for analysis of time series data and real time outbreak detection.

To retrospectively ascertain whether a parallel or consensus monitoring approach is better suited to multistream surveillance of influenza in Ontario.

The purpose of this study was to determine if existing chief complaint and ICD-9 codes for detecting gastrointestinal syndrome correctly identify similar patterns of illness when applied to the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE IV).

The 2003 outbreak of severe acute respiratory syndrome (SARS) in Taiwan provided accelerated us to develop the most timely surveillance system1. Taipei, a metropolitan with many travelers annually, requires the earliest warningand immediate responses once novel agents would attack. Considering international exchanges of epidemiological information for travelers and possible cross-country spread of EID,we initiated an ED-SSS using clinical data involving checklist CoCo and ICD-9 plus IT internally installed mechanism integrated with epidemiological information to increase the sensitivity and timeliness to detect unusual outbreaks. Objective: To face challenges of emerging infectious diseases (EID) and bioterrorism and to prepare for international collaboration without language barriers, we established a timely hospital emergency department-based syndromic surveillance system (ED-SSS) using both triage predefined check-list chief complaints (CoCo) and International Classification of Diseases, 9th Revision (ICD-9) in Taipei. The aims of this study are: (1) to monitor the patterns and trends of Taiwan’s important infectious diseases using different syndrome groups [gastrointestinal (GI), respiratory, enteroviral infections, etc.]; (2) to integrate epidemiological attributes, syndrome groups and lab. findings for improving the sensitivity, specificity and timeliness of ED-SSS in detecting outbreaks; and (3) to compare the sensitivity, specificity, and kappa value of GI, respiratory, enteroviral and central nervous system (CNS) infections between CoCo and ICD-9.

The University of Washington's Center for Public Health Informatics, in collaboration with the Kitsap County Health District and the UW Clinical Informatics Research Group, has developed the Peninsula Syndromic Surveillance Information Collection System (SSIC), a complex second-generation [1,2] distributed database system which collects heterogeneous data from three emergency department / urgent care facilities computerized electronic admission and discharge diagnosis data. We transform heterogeneous institution-specific data to a standardized XML (eXtensible Markup Language) format, which is then transmitted to and integrated into a central database. Aberration detection algorithms are used to analyze this data so that public health officials can detect higher-than-usual incidences of the clinical syndromes under surveillance.

This paper describes three years of electronic health record (EHR) data from a network of urban ambulatory care clinics in New York City.

This paper describes a study of various aberration detection algorithms currently used in syndromic surveillance and one based on artificial neural networks developed at Guelph. The goal of the research is not to select one ìwinningî algorithm but to instead understand the characteristics of the algorithms so that a systems designer can successfully use all of these algorithms in an outbreak detection system.

The purpose of this study was to compare the 2005- 2006 and 2006-2007 Influenza seasons using Influenza-like illness (ILI) data received from Emergency Departments in Miami-Dade County.

To compare the ability to detect disease outbreaks of separate and combined data streams from ambulatory care and emergency department from Harvard Pilgrim Health Care.

This paper will use CDCís EARS-X to examine Tele-healthís potential as an early warning system specifically for influenza-like illness compared to NACRS, as well as qualitatively comparing the resultant EARS flags to peaks in influenza activity identified by the Public Health Agency of Canadaís (PHAC) Federal Influenza surveillance system (Fluwatch).