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Title: A Conceptual Architecture for National Biosurveillance: Moving Beyond Situational Awareness to Enable Digital Detection of Emerging Threats

Abstract

Despite numerous calls for improvement, the U.S. biosurveillance enterprise remains a patchwork of uncoordinated systems that fail to take advantage of the rapid progress in information processing, communication, and analytics made in the past decade. By synthesizing components from the extensive biosurveillance literature, we propose a conceptual framework for a national biosurveillance architecture and provide suggestions for implementation. The framework differs from the current federal biosurveillance development pathway in that it is not focused on systems useful for “situational awareness,” but is instead focused on the long-term goal of having true warning capabilities. Therefore, a guiding design objective is the ability to digitally detect emerging threats that span jurisdictional boundaries, because attempting to solve the most challenging biosurveillance problem first provides the strongest foundation to meet simpler surveillance objectives. Core components of the vision are: (1) a whole-of-government approach to support currently disparate federal surveillance efforts that have a common data need, including those for food safety, vaccine and medical product safety, and infectious disease surveillance; (2) an information architecture that enables secure, national access to electronic health records, yet does not require that data be sent to a centralized location for surveillance analysis; (3) an inference architecture that leveragesmore » advances in ‘big data’ analytics and learning inference engines—a significant departure from the statistical process control paradigm that underpins nearly all current syndromic surveillance systems; and, (4) an organizational architecture with a governance model aimed at establishing national biosurveillance as a critical part of the U.S. national infrastructure. Although it will take many years to implement, and a national campaign of education and debate to acquire public buy-in for such a comprehensive system, the potential benefits warrant increased consideration within the U.S. government.« less

Authors:
 [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE; Department of Homeland Security Science and Technology Directorate
OSTI Identifier:
1262173
Report Number(s):
LLNL-JRNL-680437
Journal ID: ISSN 2326-5094
Grant/Contract Number:
AC52-07NA27344
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Health Security
Additional Journal Information:
Journal Volume: 14; Journal Issue: 3; Journal ID: ISSN 2326-5094
Country of Publication:
United States
Language:
English
Subject:
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE; 97 MATHEMATICS AND COMPUTING

Citation Formats

Velsko, Stephan, and Bates, Thomas. A Conceptual Architecture for National Biosurveillance: Moving Beyond Situational Awareness to Enable Digital Detection of Emerging Threats. United States: N. p., 2016. Web. doi:10.1089/hs.2015.0063.
Velsko, Stephan, & Bates, Thomas. A Conceptual Architecture for National Biosurveillance: Moving Beyond Situational Awareness to Enable Digital Detection of Emerging Threats. United States. doi:10.1089/hs.2015.0063.
Velsko, Stephan, and Bates, Thomas. Fri . "A Conceptual Architecture for National Biosurveillance: Moving Beyond Situational Awareness to Enable Digital Detection of Emerging Threats". United States. doi:10.1089/hs.2015.0063. https://www.osti.gov/servlets/purl/1262173.
@article{osti_1262173,
title = {A Conceptual Architecture for National Biosurveillance: Moving Beyond Situational Awareness to Enable Digital Detection of Emerging Threats},
author = {Velsko, Stephan and Bates, Thomas},
abstractNote = {Despite numerous calls for improvement, the U.S. biosurveillance enterprise remains a patchwork of uncoordinated systems that fail to take advantage of the rapid progress in information processing, communication, and analytics made in the past decade. By synthesizing components from the extensive biosurveillance literature, we propose a conceptual framework for a national biosurveillance architecture and provide suggestions for implementation. The framework differs from the current federal biosurveillance development pathway in that it is not focused on systems useful for “situational awareness,” but is instead focused on the long-term goal of having true warning capabilities. Therefore, a guiding design objective is the ability to digitally detect emerging threats that span jurisdictional boundaries, because attempting to solve the most challenging biosurveillance problem first provides the strongest foundation to meet simpler surveillance objectives. Core components of the vision are: (1) a whole-of-government approach to support currently disparate federal surveillance efforts that have a common data need, including those for food safety, vaccine and medical product safety, and infectious disease surveillance; (2) an information architecture that enables secure, national access to electronic health records, yet does not require that data be sent to a centralized location for surveillance analysis; (3) an inference architecture that leverages advances in ‘big data’ analytics and learning inference engines—a significant departure from the statistical process control paradigm that underpins nearly all current syndromic surveillance systems; and, (4) an organizational architecture with a governance model aimed at establishing national biosurveillance as a critical part of the U.S. national infrastructure. Although it will take many years to implement, and a national campaign of education and debate to acquire public buy-in for such a comprehensive system, the potential benefits warrant increased consideration within the U.S. government.},
doi = {10.1089/hs.2015.0063},
journal = {Health Security},
number = 3,
volume = 14,
place = {United States},
year = {Fri Jun 17 00:00:00 EDT 2016},
month = {Fri Jun 17 00:00:00 EDT 2016}
}

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