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Software

Description

Real-world public health data often provide numerous challenges. There may be a limited amount of background data, data dropouts, noise, and human error. The data from an emergency department (ED) in Urbana, IL includes a diagnosis field with multiple terms and notes separated by semicolons. There are over 7000 distinct terms, excluding the notes. Because it begins in April 2009, there is not yet adequate background data to use some of the regressionbased alerting algorithms. Values for some days are missing, so we also needed an algorithm that would tolerate data dropouts. 

INDICATOR is a workflow-based biosurveillance system developed at the National Center for Supercomputing Applications. One of the fundamental concepts of INDICATOR is that the burden of cleaning and processing incoming data should be on the software, rather than on the health care providers.

 

Objective

This paper compares different approaches with classification and anomaly detection of data from an ED.

Submitted by hparton on
Description

The 2009 H1N1 novel flu pandemic demonstrates how a rapidly spreading, contagious illness can affect the world’s population in multiple ways including health, economics, education, transportation, and national security. Pandemic disease and the threat of bio-terrorism are prompting the need for a system that integrates disparate data, makes optimal use of the breadth of available health-related analysis and predictive models, and provides timely guidance to decision makers at multiple levels of responsibility.

 

Objective

Traditional real time surveillance systems such as RODS and ESSENCE have focused on the task of threat detection; however, experience with the use of these systems in pandemic and disaster response settings suggests that a more common application is threat characterization and response management. This paper describes EpiSentry: a novel second generation real-time surveillance software system under development at Lockheed Martin that uses simulation to aid in threat characterization, response management and to provide decision support for disease outbreaks or bio-terror events.

Submitted by hparton on
Description

Cost-effective, flexible and innovative tools that integrate disparate data sets and allow sharing of information between geographically dispersed collaborators are needed to improve public health surveillance practice. Gossamer Health (Good Open Standards System for Aggregating, Monitoring and Electronic Reporting of Health), http://gossamerhealth.org, is an open source system, suitable for server or "cloud" deployment, that is designed for the collection, analysis, interpretation and visualization of syndromic surveillance data and other indicators to monitor population health. The Gossamer Health system combines applied public health informatics research conducted at the University of Washington Center for Public Health Informatics and Washington State Department of Health, in collaboration with other state and local health jurisdictions, the International Society for Disease Surveillance and the Centers for Disease Control and Prevention.

 

Objective

The goal of this work is to make available to the public health community an open source system that makes available in a standards-based, modular fashion the basic tools required to conduct automated indicator-based population health surveillance. These tools may be deployed in a flexible fashion on health department servers, in the Amazon EC2 cloud, or in any combination, and are coupled through well-defined standards-based interfaces.

Submitted by elamb on
Description

Historically, it has been the role of local health departments to administer, monitor, and report flu vaccinations of its residents to the state health department. In 2009, the looming threat of an influenza outbreak (H1N1) led to the extension of the Public Readiness and Emergency Preparedness Act (PREP) (1). On June 15, 2009, Kathleen Sebelius, Secretary of Health and Human Services, assigned all entities, including organizational and individual, tort liability immunity in the distribution and administration of H1N1 vaccines (1). This extension subsequently impaired local health departments ability to capture accurate estimates of flu immunizations being administered to their respective populations. Stark County Health Department, located in Ohio, in collaboration with Kent State University's College of Public Health, designed, developed, and deployed FITS based on the urgent need of accurate population data regarding influenza immunization at the county level.

Objective

To develop and implement a web-based, county-level flu immunization record keeping system that accurately tracks non-identifiable vaccine recipients and seamlessly uploads to the state record keeping system.

Submitted by elamb on
Description

The utility of specific sources of data for surveillance, and the quality of those data, are an ingoing issue in public health(1). Syndromic surveillance is typically conducted as a secondary use of data collected as part of routine clinical practice, and as such the data can be of high quality for the clinical use but of lower quality for the purpose of surveillance. A major data quality issue with surveillance data is that of timeliness. Data used in surveillance typically arrive as a periodic process, inherently creating a delay in the availability of the data for surveillance purposes. Surveillance data are often collected from multiple sources, each with their own processes and delays, creating a situation where the data available for surveillance are accrued piecemeal.

Objective

This abstract discusses the quality issues identified in using Distribute. From 2006 to 2012, the ISDS ran Distribute (2), a surveillance system for monitoring influenza like illness (ILI) and gastroenteritis (GI) ED visits on a nationwide basis. This system collected counts for ILI, GI and total ED visits, aggregated to the level of jurisdiction. The primary data quality issue faced with the Distribute system was that of timeliness due to accrual lag; variable delays in the receipt of surveillance data from sources by jurisdictions together with variable delays in the reporting of aggregate data from jurisdictions to Distribute resulted in data which accrued over time(3).

Submitted by knowledge_repo… on
Description

Clinical and public health microbiology laboratories of the world are a rich, underutilized resource in monitoring the changing epidemiology of microbial populations worldwide. Two areas of public health importance in which effective use of relevant local data are critical include: 1. guiding local treatment guidelines, informed by knowledge of local patterns of infection and antimicrobial resistance; and 2. the early identification and characterization of outbreaks.

Most laboratories in the developed world and many in the developing world have clinical databases designed to meet the day-to-day needs of clinical reporting, specimen processing, billing, and permanent information storage. Unfortunately, most such systems were not developed with the epidemiological needs of microbiologists, infection control staff, public health authorities, and policy-makers in mind. To address this critical gap, our group at the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance has developed the WHONET and BacLink softwares to support local, national, and international infectious disease surveillance programs.

 

Objective

This paper describes two free softwares developed for the automated and semi-automated capture, processing, and analysis of microbiology laboratory data. Applications include early detection of hospital and community outbreaks, guiding local treatment guidelines and public health policy, and immediate alert of important pathogens and potential errors in laboratory testing.

Submitted by elamb on
Description

In the Northern part of Norway, all general practitioners (GPs) and hospitals use electronic health record systems. They are all connected via an independent secure IP-network called the Norwegian Health Network which enables electronic communication between all institutions involved in disease prevention and healthcare.

 

Objective

The Norwegian Centre for Telemedicine plans to establish a peer-to-peer based surveillance  network between all GPs, laboratories, accident and emergency units, and other relevant health providers and authorities in Northern Norway. This paper briefly describes the architecture and components of the system and the motivation for using this approach.

Submitted by elamb on
Description

Real-time disease surveillance is critical for early detection of the covert release of a biological threat agent (BTA). Numerous software applications have been developed to detect emerging disease clusters resulting from either naturally occurring phenomena or from occult acts of bioterrorism. However, these do not focus adequately on the diagnosis of BTA infection in proportion to the potential risk to public health.

GUARDIAN is a real-time, scalable, extensible, automated, knowledge-based BTA detection and diagnosis system. GUARDIAN conducts real-time analysis of multiple pre-diagnostic parameters from records already being collected within an emergency department. The goal of this system is to move from simple trend anomaly detection to an infectious disease specific expert system in order to assist clinicians in detecting potential BTAs as quickly and effectively as possible. GUARDIAN improves the diagnostic process for BTA infection through the capture and automated application of associated clinical expertise. The automated application of this knowledge provides the focus and accuracy necessary for effective BTA infection diagnosis. The continuity of this process improves the efficiency by which diagnoses of BTA infections can be made.

Submitted by elamb on
Description

Management of software development projects involves a collection of well understood issues which are not often found in other project management areas. Identifying and managing these issues primarily requires that the manager is aware of the potential problems which can arise while developing software and what are the appropriate measures to control such problems.

 

Objective

Interest in syndromic surveillance through automated software systems is becoming more common and with this interest is an increase in small to medium sized software development projects. This paper discusses some of the common project management problems which occur when developing software in a community which does not have a long history of working in this area.

Submitted by elamb on
Description

Infectious disease outbreaks require rapid access to information to support a coordinated response from healthcare providers and public health officials. They need to know the size, spread, and location of the outbreak, and they also need access to models that will help them to determine the best strategy to contain the outbreak. 

There are numerous software tools for outbreak detection, and there are also surveillance systems that depend on communication between health care professionals. Most of those systems use a single type of surveillance data (e.g., syndromic, mandatory reporting, or laboratory) and focus on human surveillance.

However, there are fewer options for planning responses to outbreaks. Modeling and simulation are complex and resource-intensive. For example, EpiSims and EpiCast, developed by the National Institute of Health Models of Infectious Disease Agent Study involve large, diverse datasets and require access to high-performance computing.

Cyberenvironments are an integrated set of tools and services tailored to a specific discipline that allows the community to leverage the national cyberinfrastructure in their research and teaching. They provide data stores, computational capabilities, analysis and visualization services, and interfaces to shared instruments and sensor networks.

The National Center for Supercomputing Applications is applying the concept of cyberenvironments to infectious disease surveillance to produce INDICATOR.

 

Objective

This paper describes INDICATOR, a biosurveillance cyberenvironment used to analyze hospital data and generate alerts for unusual values.

Submitted by elamb on