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Improving the Value of Analysis for Biosurveillance

Author

Listed:
  • Henry H. Willis

    () (RAND Corporation, Pittsburgh, Pennsylvania 15213)

  • Melinda Moore

    () (RAND Corporation, Arlington, Virginia 22202)

Abstract

Biosurveillance provides information that improves decisions about mitigating the effects of disease outbreaks and bioterrorism. The success of biosurveillance depends on the effectiveness of at least four key processes: data collection, data analysis and interpretation, data integration from across organizations, and action (including public responses) based upon results of the analysis. Questions typically arise about whether information from biosurveillance systems represents a threat that justifies a response. To begin answering these questions, the Institute of Medicine Standing Committee on Health Threats Resilience has been undertaking discussions of strategies that the Department of Homeland Security National Biosurveillance Integration Center could use to strengthen its decision support and decision analysis functions. As part of these discussions, this paper applies two standard decision analysis tools to biosurveillance-- decision trees and value-of-information analysis---to assess the implications of strategies to enhance biosurveillance and to improve decisions about whether and how to act after detection of a biosurveillance signal. This application demonstrates how decision analysis tools can be used to improve public health preparedness decision making by developing a road map for how best to enhance biosurveillance through better analytic tools and methods.

Suggested Citation

  • Henry H. Willis & Melinda Moore, 2014. "Improving the Value of Analysis for Biosurveillance," Decision Analysis, INFORMS, vol. 11(1), pages 63-81, March.
  • Handle: RePEc:inm:ordeca:v:11:y:2014:i:1:p:63-81
    DOI: 10.1287/deca.2013.0283
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    File URL: http://dx.doi.org/10.1287/deca.2013.0283
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    References listed on IDEAS

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    1. W. Viscusi, 2009. "Valuing risks of death from terrorism and natural disasters," Journal of Risk and Uncertainty, Springer, vol. 38(3), pages 191-213, June.
    2. Nelson, C. & Lurie, N. & Wasserman, J. & Zakowski, S., 2007. "Conceptualizing and defining public health emergency preparedness," American Journal of Public Health, American Public Health Association, vol. 97(S1), pages 9-11.
    3. Joost R. Santos & Mark J. Orsi & Erik J. Bond, 2009. "Pandemic Recovery Analysis Using the Dynamic Inoperability Input‚ÄźOutput Model," Risk Analysis, John Wiley & Sons, vol. 29(12), pages 1743-1758, December.
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    Citations

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    Cited by:

    1. Mary F. McGuire, 2014. "Pancreatic Cancer: Insights from Counterterrorism Theories," Decision Analysis, INFORMS, vol. 11(4), pages 265-276, December.
    2. Carlo Drago & Matteo Ruggeri, 2019. "Setting research priorities in the field of emergency management: which piece of information are you willing to pay more?," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 2103-2115, July.

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