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Data Analysis

Description

In France, surveillance of seasonal gastroenteritis uses to be monitored by an information system based on a computer network of physicians so called Sentinel Network (1). Regionally, the use of this system as limitations. SOS Medecin is an organization of general practitioners, present in many French cities, which undertakes home medical visits 24hrs a day, 7 days a week. In Bordeaux, this organization makes a daily transmission of every diagnostic related to their visits.

Objective

To construct an indicator adapted for syndromic surveillance of seasonal gastroenteritis based on data from "SOS Medecin" in the city of Bordeaux, France.

Submitted by elamb on
Description

National telephone health advice service data have been investigated as a source for syndromic surveillance of influenza-like illness and gastroenteritis . Providing a high level of coverage, the system might serve as an early outbreak detection tool. We have previously found that telephone triage service data of acute gastroenteritis was superior to web queries as well as over-the-counter pharmacy sales of anti-diarrhea medication to detect large water- and foodborne outbreaks of gastrointestinal illness in Sweden during the years 2007–2011 (4). However, information is limited regarding the usefulness, characteristics, and signal properties of local telephone triage data for monitoring and identifying outbreaks at the community level.

Objective

Our aim was to use telephone triage data to develop a model for community-level syndromic surveillance that can detect local outbreaks of acute gastroenteritis (AGE) and influenza-like illness (ILI) and allow targeted local disease control information.

Submitted by knowledge_repo… on
Description

The North Dakota Veterinary Diagnostic Laboratory (NDVDL) manages animal disease laboratory tests, results and diagnostic services using the software VetStar Animal Disease Diagnostic System (VADDS) (Advanced Technology Corporation, Ramsey, NJ). The North Dakota State Board of Animal Health with the Department of Agriculture, in collaboration with the North Dakota Department of Health (NDDoH), has developed an electronic laboratory reporting system using data streams exported from the VADDS system for statewide animal health and public health surveillance.

Objective

 To describe the North Dakota Electronic Animal Health Surveillance System and data analysis using the CDC EARS V4r5.

Submitted by elamb on
Description

Time series analysis is very popular in syndromic surveillance. Mostly, public health officials track in the order of hundreds of disease models or univariate time series daily looking for signals of disease outbreaks. These time series can be aggregated counts of various syndromes, possibly different genders and age-groups. Recently, spatial scan algorithms find anomalous regions by aggregating zipcode level counts [1]. Usually, public health officials have a set of disease models (for e.g. fever or headache symptom in male adults is indicative of a particular disease). Based on the past experience public health officials track these disease models daily to find anomalies that might be indicative of disease outbreaks. A typical syndromic surveillance system these days will track in the order of 100-200 time series on daily basis using different univariate algorithms like CUSUM, moving average, EWMA, etc.

Let us consider a representative dataset of a state which has 100 zipcodes that monitors 10 syndromes among 3 age groups and 2 genders in emergency rooms. There are a total of 6,000 (100 x 10 x 3 x 2) distinct time series for a particular zipcode, syndrome, age-group and gender. This number already seems too high to monitor daily. Hence most syndromic systems only monitor state level aggregates for all syndromes or a few combinations of syndromes, gender and age-groups.

But most real world disease models are more complex and affect multiple syndromes, or multiple agegroups. We need to analyze more complex streams that aggregate multiple values in the attributes to mine more interesting patterns not seen otherwise. As an example, a massive search could reveal that recently senior female patients having fever and nausea have increased in the north eastern part of the state.

Objective

This paper shows how T-Cubes, a data structure that makes tracking millions of disease models simultaneously feasible, can be used to perform multivariate time series analysis using primitive univariate algorithms. Hence, the use of T-Cube in brute-force search helps identify stronger disease outbreak signals currently missed by the surveillance systems.

Submitted by elamb on
Description

Free text chief complaints (CCs), which may be recorded in different languages, are an important data source for syndromic surveillance systems. For automated syndromic surveillance, CCs must be classified into predefined syndromic categories to facilitate subsequent data aggregation and analysis. However, CCs in different languages pose technical challenges for the development of multilingual CC classifiers.  We addressed the technical challenges by first developing a ontology-enhanced CC classifier which exploits semantic relations in the Unified Medical Language System (UMLS) to expand the knowledge of a rule-based CC classifier. Based on the ontologyenhanced English CC classifier, a translation module was incorporated to extract symptom-related information in Chinese CCs and translate it into English. This design thus enables the processing of CCs in both English and Chinese. 

Objective  

This paper describes the effort to design and implement a chief complaint (CC) classification system that is capable of processing CCs in both English and Chinese.

Submitted by elamb on
Description

In September 2004, Kingston, Frontenac and Lennox and Addington Public Health began a 2-year pilot project to develop and evaluate an Emergency Department Chief Complaint Syndromic Surveillance System in collaboration with the Ontario Ministry of Health and Long Term Care – Public Health Branch, Queen’s University, Public Health Agency of Canada, Kingston General Hospital and Hotel Dieu Hospital. At this time, the University of Pittsburgh’s Real-time Outbreak and Disease Surveillance (RODS, Version 3.0) was chosen as the surveillance tool best suited for the project and modifications were made to meet Canadian syndromic surveillance requirements. To evaluate the design and implementation of the system, a multi-sectored approach to evaluation was taken. Individual evaluations of the process, technical aspects and of cost/benefit were conducted to demonstrate proof of concept and the associated costs. An overall outcome or effectiveness evaluation will take place in spring 2006.

 

Objective

This paper outlines the approach used to evaluate an emergency department syndromic surveillance system on the following areas: process and outcome, cost/benefit and technical.

Submitted by elamb on
Description

Space-time detection of disease clusters can be a computationally intensive task which defies the real time constraint for disease surveillance. At the same time, it has been shown that using exact patient locations, instead of their representative administrative regions, result in higher detection rates and accuracy while improving upon detection timeliness. Using such higher spatial resolution data, however, further exacerbates the computational burden on real time surveillance. The critical need for real time processing and interpretation of data dictate highly responsive models that may be best achievable utilizing high performance computing platforms.

Objective

Space-time detection techniques often require computationally intense searching in both the time and space domains. We introduce a high performance computing technique for parallelizing a variation of space-time permutation scan statistic applied to real data of varying spatial resolutions and demonstrate the efficiency of the technique by comparing the parallelized performance under different spatial resolutions with that of serial computation.

Submitted by elamb on