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Recommendations for Modeling Disaster Responses in Public Health and Medicine: A Position Paper of the Society for Medical Decision Making

Author

Listed:
  • Margaret L. Brandeau

    (Department of Management Science and Engineering, Stanford University, Stanford, California)

  • Jessica H. McCoy

    (Department of Management Science and Engineering, Stanford University, Stanford, California)

  • Nathaniel Hupert

    (Department of Public Health, Weill Medical College, Cornell University, New York)

  • Jon-Erik Holty

    (Division of Pulmonary and Critical Care Medicine, Department of Medicine, Stanford University, Stanford, California)

  • Dena M. Bravata

    (Nathaniel Hupert is currently Director of the Preparedness Modeling Unit of the US Centers for Disease Control and Prevention, Center for Primary Care and Outcomes Research Stanford University, Stanford, California)

Abstract

Purpose. Mathematical and simulation models are increasingly used to plan for and evaluate health sector responses to disasters, yet no clear consensus exists regarding best practices for the design, conduct, and reporting of such models. The authors examined a large selection of published health sector disaster response models to generate a set of best practice guidelines for such models. Methods . The authors reviewed a spectrum of published disaster response models addressing public health or health care delivery, focusing in particular on the type of disaster and response decisions considered, decision makers targeted, choice of outcomes evaluated, modeling methodology, and reporting format. They developed initial recommendations for best practices for creating and reporting such models and refined these guidelines after soliciting feedback from response modeling experts and from members of the Society for Medical Decision Making. Results . The authors propose 6 recommendations for model construction and reporting, inspired by the most exemplary models: health sector disaster response models should address real-world problems, be designed for maximum usability by response planners, strike the appropriate balance between simplicity and complexity, include appropriate outcomes that extend beyond those considered in traditional cost-effectiveness analyses, and be designed to evaluate the many uncertainties inherent in disaster response. Finally, good model reporting is particularly critical for disaster response models. Conclusions . Quantitative models are critical tools for planning effective health sector responses to disasters. The proposed recommendations can increase the applicability and interpretability of future models, thereby improving strategic, tactical, and operational aspects of preparedness planning and response.

Suggested Citation

  • Margaret L. Brandeau & Jessica H. McCoy & Nathaniel Hupert & Jon-Erik Holty & Dena M. Bravata, 2009. "Recommendations for Modeling Disaster Responses in Public Health and Medicine: A Position Paper of the Society for Medical Decision Making," Medical Decision Making, , vol. 29(4), pages 438-460, July.
  • Handle: RePEc:sae:medema:v:29:y:2009:i:4:p:438-460
    DOI: 10.1177/0272989X09340346
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    References listed on IDEAS

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    Cited by:

    1. Damon J A Toth & Adi V Gundlapalli & Wiley A Schell & Kenneth Bulmahn & Thomas E Walton & Christopher W Woods & Catherine Coghill & Frank Gallegos & Matthew H Samore & Frederick R Adler, 2013. "Quantitative Models of the Dose-Response and Time Course of Inhalational Anthrax in Humans," PLOS Pathogens, Public Library of Science, vol. 9(8), pages 1-18, August.
    2. Joost R. Santos & Lucia Castro Herrera & Krista Danielle S. Yu & Sheree Ann T. Pagsuyoin & Raymond R. Tan, 2014. "State of the Art in Risk Analysis of Workforce Criticality Influencing Disaster Preparedness for Interdependent Systems," Risk Analysis, John Wiley & Sons, vol. 34(6), pages 1056-1068, June.
    3. Michael A. Hamilton & Tao Hong & Elizabeth Casman & Patrick L. Gurian, 2015. "Risk‐Based Decision Making for Reoccupation of Contaminated Areas Following a Wide‐Area Anthrax Release," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1348-1363, July.
    4. Liu, Kanglin & Li, Qiaofeng & Zhang, Zhi-Hai, 2019. "Distributionally robust optimization of an emergency medical service station location and sizing problem with joint chance constraints," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 79-101.

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