Surveillance Systems

In Sri Lanka, a major drawback in injury prevention is the lack of complete, accurate and timely data. To fulfill this data need, in 2006, Sri Lanka's Trauma Secretariat piloted an Injury Surveillance System (ISS) in four hospitals. This comprised of two parts: a paper-based data collection tool (Trauma Surveillance Record or TSR) and its corresponding software application. TSR recorded ICD-10 Chapter XIX codes related to the diagnoses of injuries, but did not record the Chapter XX codes pertaining to external causes of morbidity which provide essential knowledge for injury prevention. The software application was built using proprietary technologies that could lead to increased costs and associated dependencies with vendors. The system was unable to comply with the changing data needs of the Ministry of Health (MoH) without a major retooling. Consequently, in 2011, the MoH made changes in the TSR, but the software application was unable to handle those changes. The ISS was evaluated by three independent teams which recommended discontinuing its use and suggested the development of a new system.

Objective

Designing, developing and piloting a web-based Injury Surveillance System for Sri Lanka.

Much attention has been given recently to the purported ability of social media to provide early warning and/or situational awareness and event characterization during a biological event of national concern. The National Biosurveillance Integration Center's (NBIC) innovation project on Social Media Analysis seeks to demonstrate the viability of extracting relevant, health information from social media data, with the ultimate goal to establish an operational social media system for biological event surveillance. Early work in this project has focused on demonstrating the relevance of social media to the biosurveillance problem through data analysis and algorithm development. Preliminary assessments of a commercial social media product also yielded valuable insights for the system architecture required to support such an operational tool. In addition to continued analysis of data utility (algorithm development) and system architecture, future work will include development of a comprehensive concept of operations (CONOPS) for implementation and use of a social media capability within the NBIC.

Objective

Through ongoing and future projects we will examine the utility of social media data for biosurveillance, including machine learning approaches for algorithm development, as well as the system and organizational architectures required to implement an operational system.

The goal of adequate biosurveillance is to signal that an outbreak may be occurring and through subsequent work is confirmed or refuted. Such a system should be equally able to detect outbreaks of diseases of extremely low reporting frequency, or those with high seasonality. Methods of detecting increases in notifiable communicable diseases reported to the Missouri Department of Health and Senior Services (MDHSS) were based on quartile comparisons to 5-year historical disease reports for the report week and resulted in frequent detection of statistically significant increases that were, in fact, not indicative of disease outbreaks. Frequently generated alerts led to 'alarm fatigue' in epidemiologists.

Objective

Develop a statistically rigorous automated process for weekly communicable disease report analysis to improve the speed and accuracy of outbreak detection in Missouri.

COMBS is facilitating analyst workflows and collaboration, greatly accelerating the management of a bio-event, effectively implementing new capabilities and technologies, and providing opportunities for a wide variety of organizations to contribute data and tools that support their own goals while supporting and governing the ecosystem collaboratively.

Objective

The goal of DTRA's Biosurveillance Ecosystem (BSVE) program is to significantly reduce the time required to identify threats to human health and respond appropriately. The Draper Team is developing the Collaborative Overarching Multi-feed Biosurveillance System (COMBS) for BSVE to revolutionize biosurveillance (BSV) capabilities. Analysts will benefit from rapid and thorough information access, as will local public health authorities and individual citizens.

Syndromic surveillance has great advantages in promoting the early detection of infectious disease outbreak and enabling the real-time tracking of on-going epidemics. However, establishing a syndromic surveillance system required huge investment in money, information system, manpower and capacity building activities, which remains a big challenge in resource-limited areas. Funded by European Union's 7th Framework Programme, a syndromic surveillance system named 'ISSC' was prepared to be built and incorporated with the existing case report system in rural Jiangxi Province of China.

Objective

Before the start of ISSC project, a pilot investigation was carried out among the candidate surveillance units (health care facility, pharmacy and primary school) and related stakeholders to assess their capacity and potential needs with regard to the implementation of ISSC system, so as to design customized capacity building and training strategies.

Multiple data sources are used in a variety of biosurveillance systems. With the advent of new technologies, globalization, high performance computing, and "big data" opportunities, there are seemingly unlimited potential data streams that could be useful in biosurveillance. Data streams have not been universally defined in either the literature or by specific biosurveillance systems. The definitions and framework that we have developed enable a characterization methodology that facilitates understanding of data streams and can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities- filling a gap recognized in both the public health and biosurveillance communities.

Objective

To develop a data stream-centric framework that can be used to systematically categorize data streams useful for biosurveillance systems, supporting comparative analysis

Developing countries bear the highest burden of infectious diseases, and therefore play a key role in the detection of emerging disease threats. The ability of these countries to detect such events allows an adequate response preventing its spread and reducing the morbidity and mortality of communities across the world. Due to the importance of strengthening the surveillance capabilities of developing countries, resources need to be dedicated to this effort. International organizations often provide technical and financial support when countries need assistance. When implementing such systems, the countries and supporting organizations face challenges not limited to scarce resources. These countries often also face political and social instability, and cultural and religious practices that may put their populations at greater risk of these events. In addition, the disease surveillance field lacks clear guidance on how to initiate or improve the implementation process for these systems. The experiences described in the literature are often presented as partial evaluations using the US Centers for Disease Control and Prevention guidelines, without providing an explanation of the key factors that are related to successful or unsuccessful experiences in such settings. Public health practitioners at the ministries of health (MOH) and international organizations would greatly benefit from a model that considers these factors to serve as a guideline for a positive implementation outcome and an effective use of resources.

Objective

The panel will present successful and unsuccessful practices, challenges and lessons learned when implementing electronic surveillance tools in the developing world, and a best practices model that aims to guide and facilitate this process. Panelists and audience will discuss the validity of this model, and identify potential countries where it could be tested.

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.

Syndromic surveillance can provide early warning of potential public health emergencies and acute health events in a population. The sharing and aggregation of syndromic data among jurisdictions can provide more comprehensive situational awareness and improve coordination and decision-making. The BioSense 2.0 Program supports increased syndromic data-sharing among a nationwide network of local and state public health agencies. Most users of this application utilize the main web site front-door interface due to its user-friendly features for query and analysis. However, this interface currently has a limited number of analytic tools, export functions, and provides access only to binned data. The back-door interface, with access to additional data lockers containing raw and exception data, represents a potentially rich source of untapped and underutilized information. In this presentation, we discuss our ongoing development and early success of a capacity (consisting of code libraries, a parser, and an implementation guide) that allows users to tailor a program-specific, automated process for generating surveillance reports from their BioSense 2.0 data locker. The product will soon be available to members of the BioSense User Community.

Objective

The purpose of this project is to develop a capacity to facilitate implementation of a user-driven enhanced process for generating program-specific surveillance reports from BioSense 2.0 locker data.

The Johns Hopkins Applied Physics Laboratory and the Armed Forces Health Surveillance Center have developed a hybrid processing engine that alerts monitors when a severe health condition exists based on corroboration among several sources of data. The system was designed to ingest a day's worth of recent data and provide results to monitors daily. In some theaters, the health of the US Forces must be determined at near-real time rates requiring a reassessment of current surveillance practices. Challenges exist in both acquiring data in real-time and in modifying automated alerting processes to re-evaluate as a new piece of evidence is received.

Objective

To develop a real-time surveillance capability that processes, fuses and assesses when data is received using a new fusion processing methodology and multiple sources health indicator data.