A Systematic Evaluation of Traditional and Non-Traditional Data Streams for Integrated Global Biosurveillance – Final Report

Deshpande, Alina; Brown, M. G.; Castro, Lauren; Daniel, W. Brent; Generous, Nicholas; Hengartner, Andrea; Longo, Joseph F.; Margevicius, Kristen; Mccabe, Kirsten; Parliman, Zachary A.

doi:10.2172/1077029

2013

DOI: 10.2172/1077029

|View full text |Cite

A Systematic Evaluation of Traditional and Non-Traditional Data Streams for Integrated Global Biosurveillance – Final Report

Alina Deshpande¹,

M. G. Brown²,

Lauren Castro³

et al.

Abstract: Data that leads to identification of a pathogen, or confirmed diagnosis of disease Syndromic Health-related data that may precede or substitute for formal diagnosis Environmental Pertaining to data streams that are not related to health parameters, such as social, natural, and built environments Context for data stream Data stream described by

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2013

2014

Publication Types

Select...

Article4

Relationship

Self Cite2

Independent2

Authors

Journals

Cited by 4 publications

References 10 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Advancing a Framework to Enable Characterization and Evaluation of Data Streams Useful for Biosurveillance

et al. 2014

Self Cite

View full text Add to dashboard Cite

In recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.

show abstract

Advancing a Framework to Enable Characterization and Evaluation of Data Streams Useful for Biosurveillance

et al. 2014

Self Cite

View full text Add to dashboard Cite

show abstract

Selecting Essential Information for Biosurveillance—A Multi-Criteria Decision Analysis

et al. 2014

Self Cite

View full text Add to dashboard Cite

The National Strategy for Biosurveillancedefines biosurveillance as “the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels.” However, the strategy does not specify how “essential information” is to be identified and integrated into the current biosurveillance enterprise, or what the metrics qualify information as being “essential”. Thequestion of data stream identification and selection requires a structured methodology that can systematically evaluate the tradeoffs between the many criteria that need to be taken in account. Multi-Attribute Utility Theory, a type of multi-criteria decision analysis, can provide a well-defined, structured approach that can offer solutions to this problem. While the use of Multi-Attribute Utility Theoryas a practical method to apply formal scientific decision theoretical approaches to complex, multi-criteria problems has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance.We have developed a formalized decision support analytic framework that can facilitate identification of “essential information” for use in biosurveillance systems or processes and we offer this framework to the global BSV community as a tool for optimizing the BSV enterprise. To demonstrate utility, we applied the framework to the problem of evaluating data streams for use in an integrated global infectious disease surveillance system.

show abstract

A Review of Evaluations of Electronic Event-Based Biosurveillance Systems

et al. 2014

View full text Add to dashboard Cite

Electronic event-based biosurveillance systems (EEBS’s) that use near real-time information from the internet are an increasingly important source of epidemiologic intelligence. However, there has not been a systematic assessment of EEBS evaluations, which could identify key uncertainties about current systems and guide EEBS development to most effectively exploit web-based information for biosurveillance. To conduct this assessment, we searched PubMed and Google Scholar to identify peer-reviewed evaluations of EEBS’s. We included EEBS’s that use publicly available internet information sources, cover events that are relevant to human health, and have global scope. To assess the publications using a common framework, we constructed a list of 17 EEBS attributes from published guidelines for evaluating health surveillance systems. We identified 11 EEBS’s and 20 evaluations of these EEBS’s. The number of published evaluations per EEBS ranged from 1 (Gen-Db, GODsN, MiTAP) to 8 (GPHIN, HealthMap). The median number of evaluation variables assessed per EEBS was 8 (range, 3–15). Ten published evaluations contained quantitative assessments of at least one key variable. No evaluations examined usefulness by identifying specific public health decisions, actions, or outcomes resulting from EEBS outputs. Future EEBS assessments should identify and discuss critical indicators of public health utility, especially the impact of EEBS’s on public health response.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Systematic Evaluation of Traditional and Non-Traditional Data Streams for Integrated Global Biosurveillance – Final Report

Cited by 4 publications

References 10 publications

Advancing a Framework to Enable Characterization and Evaluation of Data Streams Useful for Biosurveillance

Advancing a Framework to Enable Characterization and Evaluation of Data Streams Useful for Biosurveillance

Selecting Essential Information for Biosurveillance—A Multi-Criteria Decision Analysis

A Review of Evaluations of Electronic Event-Based Biosurveillance Systems

Contact Info

Product

Resources

About