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Dasey Timothy

Public health surveillance relies on multiple systems and methodologies for data collection, analysis, and interpretation. Each component provides only part of the picture, such as detection of possible outbreaks or events of concern; geographic profiles or time courses of disease activity; or indicators of clinical severity by age, risk factors, etc. Novel, unstructured data sources like Twitter feeds and aggregated news reports are growing as a source of information about health and disease. What and where are the contributions of these nontraditional, often non-specific, data types to BSV? The answer will depend on the purpose and target population. Different data streams often have greater utility for one BSV function (e.g., outbreak detection) than another (e.g., situation awareness). Furthermore, public health agencies at different levels need and use data differently, as determined by their priorities for public health. New types of data can also be useful for disease prediction and forecasting, pandemic modeling, and developing analytic tools. Before any new data modality can be integrated into standards of surveillance practice, it needs to be evaluated for its contribution to understanding disease activity and the value added when compared to other sources of data with regard to validity, timeliness, accuracy, representativeness, and positive and negative predictive values. Furthermore, questions remain about when novel, unstructured, or nontraditional data sources are acceptable evidence to inform decision-making and public health actions. To address this, the strengths and weaknesses of different types of data for various surveillance functions need to be discussed among stakeholders that bring various perspectives from surveillance research, practice, and policy.

Objective

To gather thought leaders in informatics, public health practice, surveillance research, and strategic decision-making to provide their insights into where and how to effectively integrate novel data streams, such as social media, into biosurveillance (BSV) systems and standards of public health surveillance practice.

Submitted by knowledge_repo… on

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.

Submitted by uysz on

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.

Submitted by uysz on

Early detection of a disease outbreak using pre-diagnostic textual data is available in biosurveillance systems with the integration of data such as chief complaints. Social media has been identified as an additional pre-diagnostic data source of interest. Textual data analysis in public health is usually based on a keyword search and often involves a complex Boolean combination of terms that produce results with many false alarms. Epidemiologists may wish to query the data differently based on the event of interest, yet the process is laborious to weed out uninteresting content. Specialized detectors that decide on the topical relevance of keyword search usually require developers to adapt methods to new uses, which is a time- and cost-prohibitive activity. Users need the ability to rapidly build text content detectors on their own.

Objective

To demonstrate a framework for user-customizable text processing that can improve the efficiency and effectiveness of mining text for biosurveillance, with initial application to Twitter.

Submitted by teresa.hamby@d… on

This webinar will provide an overview of game-based tools for surveillance training and technology evaluation. The philosophy and methods of “serious gaming” will be presented through case studies and interactive examples.

Panelists

Timothy Dasey, PhD, Group Leader, Chemical and Biological Defense Systems Group, MIT Lincoln Laboratories

Date and Time

Thursday, October 27, 2011

Host

ISDS Public Health Practice and Research Committees

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