Surveillance Systems

Currently, there’s little effective communication and collaboration among public health departments. The lack of collaboration has resulted in more than 300 separate biosurveillance systems, which are disease specific, not integrated or interoperable, and may be duplicative. Grid architecture is a promising methodology to aid in building a decentralized health surveillance infrastructure because it encourages an ecosystem development culture, which has the potential to increase collaboration and decrease duplications.

Objective

This poster describes an approach which leverages grid technology for the epidemiological analysis of public health data. Through a virtual environment, users, particularly epidemiologists, and others unfamiliar with the application, can perform on-demand powerful statistical analyses.

Outbreaks of waterborne gastrointestinal disease occur routinely in North America, resulting in considerable morbidity, mortality, and cost (Hrudey, Payment et al. 2003). Outbreak detection methods generally attempt to identify anomalies in time, but do not identify the type or source of an outbreak. We seek to develop a framework for both detection and classification of outbreaks using information in both space and time. Outbreak detection can be improved by using simulated outbreak data to build, validate, and evaluate models that aim to improve accuracy and timeliness of outbreak detection.

Objective

To develop a methodological framework for detecting and classifying outbreaks of gastrointestinal disease on the island of Montreal, with the goal of improving early outbreak detection using simulated surveillance data.

In response to the 2009 H1N1 pandemic, the Early Warning Infectious Disease Surveillance Program Office of Binational Border Health in the California Department of Public Health sought to strengthen outpatient ILI surveillance along the CA/BC border by creating the first binational influenza surveillance network in the region. The establishment of this network was crucial for enhancing cross-border situational awareness of influenza activity, especially in a region characterized by high levels of population mobility.

Objective

To enhance cross-border surveillance for Influenza-Like-Illness (ILI) in the California/Baja California (CA/BC) border region through the formation of a border binational surveillance network.

Recently, a growing number of studies have made use of Twitter to track the spread of infectious disease. These investigations show that there are reliable spikes in traffic related to keywords associated with the spread of infectious diseases like Influenza [1], as well as other Syndromes [2]. However, little research has been done using Social Media to monitor chronic conditions like Asthma, which do not spread from sufferer to sufferer. We therefore test the feasibility of using Twitter for Asthma surveillance, using techniques from NLP and machine learning to achieve a deeper understanding of what users Tweet about Asthma, rather than relying only on keyword search.

Objective

We present a Content Analysis project using Natural Language Processing to aid in Twitter-based syndromic surveillance of Asthma

An objective of the Joint VA/DoD BioSurveillance System for Emerging Biological Threats project is to improve situational awareness of the health of combined VA and DoD populations. DoD and VA both use versions of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). With a retrospective outpatient data collection available, we analyzed relative coverage and timeliness of the two systems to understand potential benefits of a joint system.

Objective

We determined the utility and effective methodology for combin- ing patient record information from the Departments of Veterans Af- fairs (VA) and Defense (DoD) health surveillance systems. 

The Defense Threat Reduction Agency Chemical and Biological Technologies Directorate (DTRA CB) has initiated the Biosurveillance Ecosystem (BSVE) research and development program. Work process flow diagrams, with associated explanations and historical examples, were developed based on in-person, structured interviews with public health and preventative medicine analysts from a variety of Department of Defense (DoD) organizations, and with one organization in the Department of Health and Human Services (DHHS) and with a major U.S. city health department. The particular nuanced job characteristics of each organization were documented and subsequently validated with the individual analysts. Additionally, the commonalities across different organizations were described in meta-workflow diagrams and descriptions.

Objective

Operational biosurveillance capability gaps were analyzed and the required characteristics of new technology were outlined, the results of which will be described in this contribution.

Next-generation software environments for disease surveillance will need to have several important characteristics, among which are collaboration and search and discovery features, access to various data sets, and a variety of analytic methods. However, perhaps the most important feature is the least often mentioned – the ability to have the system adapt over time without high reengineering cost. The public health community cannot afford software redesigns every few years as data sets expand, analysis needs evolve, and software deficiencies are exposed. In addition to the need to adapt an environment over longer time periods, epidemiologists have high variability in their day-to-day needs that require adaptability over short time periods as well. Each outbreak or health situation has unique aspects, and analysts need to be able to bring in data and methods unique to that situation that may not be easily anticipated a priori. The most common approach to increasing reusability and decreasing upgrade costs are open architecture software frameworks such as Service-Oriented Architectures (SOAs). If well implemented, SOAs can significantly reduce software upgrade costs by allowing services (a software module) to be easily swapped out for improvements or supplemented with additional services. SOAs can help with long-term adaptability, but are not useful in short-term adaptability, since the software development team must be engaged in each cycle. Another approach is to include an App Store. Unfortunately, App Stores for government use have often been disappointing. Apps can tend to be quite simple, and even slight changes from what is programmed – a predictable situation with the variability seen in disease surveillance realm - will result in an epidemiologist having to get a software developer to make them a new App.

Objective:

This abstract discusses the BioAFTER project, which builds upon SOA and App Store concepts by allowing Apps to be strung together in unique combinations, according to the problem of the day.

Salmonellosis is the zoonotic disease caused by Salmonella bacteria. These are food-borne pathogens, which require improvement of diagnostics and surveillance measures. Prior to implementation of a PCR-based system for monitoring Salmonella, presence and differentiation of the agent was validated under Office International Epizootical (O.I.E.) requirements.

Objective

This study aimed to perform interlaboratory testing and clarification of the PCR-based test for its implementation in Ukraine.