Syndromic Surveillance

To determine the predictive abilities of systematic emergency room chief complaints for the detection of acute infectious diseases in the Winnipeg population.

58 medical licensure boards require between 12 and 50 hours of Continuing Medical Education (CME) for re-licensure of physicians. 28 states as well as Puerto Rico, the U.S. Virgin Islands, and the Mariana Islands, require continuing nursing education (CNE) for nursing re-licensure, with requirements varying from 5 hours per year to 45 hours every 3 years. Continuing education requirements may include self-directed educational programs, academic education, or research and professional activities. To the best of our knowledge, although there are online public health preparedness programs and journal articles that provide continuing education credits, there is no currently available online course on syndromic surveillance available for CME or CNE.

Objective

The Education and Training Committee of the International Society for Disease Surveillance is developing an introductory online CME curriculum in syndromic surveillance for physicians and other health practitioners. This curriculum would also be available for public health practitioners new to syndromic surveillance. The goal of the curriculum is to provide an introductory knowledge of syndromic surveillance for interested practitioners and stimulate healthcare provider cooperation and involvement with syndromic surveillance.

This paper investigates the use of data-adaptive multivariate statistical process control (MSPC) charts for outbreak detection using real-world syndromic data. The widely used EARS [1] methods and other adaptive implementations assume implicitly that nonsta-tionarity and/or the lack of historic data preclude the conventional Phase I/Phase II approach of SPC. This work examines that assumption formally by evaluating and comparing the false alarm rates and sensitivity of adaptive and non-adaptive MSPC charts applied to simulated outbreaks injected into both desea-sonalized and raw data.

The objectives of the two day International Society for Disease Surveillance (ISDS) funded consultation were to develop expert, consensus-based recommendations that address specific, unanswered questions that hinder advances in cross border syndromic surveillance. The consultation included a discussion of the details of existing Canadian (Can) and United States (US) syndromic surveillance systems and the opportunities and challenges for new developments. Particular focus was placed on the ability to detect and respond to a bioterrorism event or infectious disease outbreak across borders.

This panel member consultation is an International Society of Disease Surveillance (ISDS) sponsored project. It involved expert personnel in their respective area to address specific, priority questions confronting researchers, developers, and public health practioners in the field of syndromic surveillance (SS). The objective of this consultation will be to develop expert, consensus-based recommendations that address specific, unsettled problems or unanswered questions that hinder advances in utilization of syndromic surveillance data in combination with other data sources. Recommendations arising from the consultation should facilitate efforts by researchers, developers, or practitioners to be able to stride ahead and make progress.

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.

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).

The objective of this paper is to examine the utility of Emergency Department and Telehealth data for Syndromic Surveillance. This works attempts to minimize false outbreak detection. It also demonstrates that these two data sources contain independent information which is useful for outbreak detection.

This paper evaluates the operating characteristics of limited baseline aberration detection methods using different lengths (7-28 days) and end dates (1-7 days prior to the current day) for the baseline period using simulated outbreaks added to real data and simulated data representative of real data.

To measure the impact, within administrative geographic areas, of household income on rates of visits due influenza and RSV among children using real-time syndromic surveillance.