IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v29y2009i4p438-460.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X09340346
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X09340346?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Matt Dombroski & Baruch Fischhoff & Paul Fischbeck, 2006. "Predicting Emergency Evacuation and Sheltering Behavior: A Structured Analytical Approach," Risk Analysis, John Wiley & Sons, vol. 26(6), pages 1675-1688, December.
    2. Xinzhi Zhang & Martin I. Meltzer & Pascale M. Wortley, 2006. "FluSurge—A Tool to Estimate Demand for Hospital Services during the Next Pandemic Influenza," Medical Decision Making, , vol. 26(6), pages 617-623, November.
    3. Jotshi, Arun & Gong, Qiang & Batta, Rajan, 2009. "Dispatching and routing of emergency vehicles in disaster mitigation using data fusion," Socio-Economic Planning Sciences, Elsevier, vol. 43(1), pages 1-24, March.
    4. Jamie Dekle & Mariel S. Lavieri & Erica Martin & Hülya Emir-Farinas & Richard L. Francis, 2005. "A Florida County Locates Disaster Recovery Centers," Interfaces, INFORMS, vol. 35(2), pages 133-139, April.
    5. Vittoria Colizza & Alain Barrat & Marc Barthelemy & Alain-Jacques Valleron & Alessandro Vespignani, 2007. "Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions," PLOS Medicine, Public Library of Science, vol. 4(1), pages 1-16, January.
    6. Eva Lee & Siddhartha Maheshwary & Jacquelyn Mason & William Glisson, 2006. "Decision support system for mass dispensing of medications for infectious disease outbreaks and bioterrorist attacks," Annals of Operations Research, Springer, vol. 148(1), pages 25-53, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dean, Matthew D. & Nair, Suresh K., 2014. "Mass-casualty triage: Distribution of victims to multiple hospitals using the SAVE model," European Journal of Operational Research, Elsevier, vol. 238(1), pages 363-373.
    2. S. M. Mniszewski & S. Y. Del Valle & P. D. Stroud & J. M. Riese & S. J. Sydoriak, 2008. "Pandemic simulation of antivirals + school closures: buying time until strain-specific vaccine is available," Computational and Mathematical Organization Theory, Springer, vol. 14(3), pages 209-221, September.
    3. Floriana Gargiulo & Sônia Ternes & Sylvie Huet & Guillaume Deffuant, 2010. "An Iterative Approach for Generating Statistically Realistic Populations of Households," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-9, January.
    4. Teruhiko Yoneyama & Sanmay Das & Mukkai Krishnamoorthy, 2012. "A Hybrid Model for Disease Spread and an Application to the SARS Pandemic," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-5.
    5. A. Anaya-Arenas & J. Renaud & A. Ruiz, 2014. "Relief distribution networks: a systematic review," Annals of Operations Research, Springer, vol. 223(1), pages 53-79, December.
    6. Diehlmann, Florian & Klein, Miriam & Wiens, Marcus & Lüttenberg, Markus & Schultmann, Frank, 2020. "On the value of accurate demand information in public-private emergency collaborations," Working Paper Series in Production and Energy 51, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    7. David Simchi-Levi & Nikolaos Trichakis & Peter Yun Zhang, 2019. "Designing Response Supply Chain Against Bioattacks," Operations Research, INFORMS, vol. 67(5), pages 1246-1268, September.
    8. Wilson, Duncan T. & Hawe, Glenn I. & Coates, Graham & Crouch, Roger S., 2013. "A multi-objective combinatorial model of casualty processing in major incident response," European Journal of Operational Research, Elsevier, vol. 230(3), pages 643-655.
    9. Kılcı, Fırat & Kara, Bahar Yetiş & Bozkaya, Burçin, 2015. "Locating temporary shelter areas after an earthquake: A case for Turkey," European Journal of Operational Research, Elsevier, vol. 243(1), pages 323-332.
    10. Joseph H. Cook, 2013. "Principles and standards for benefit–cost analysis of public health preparedness and pandemic mitigation programs," Chapters, in: Scott O. Farrow & Richard Zerbe, Jr. (ed.), Principles and Standards for Benefit–Cost Analysis, chapter 3, pages 110-152, Edward Elgar Publishing.
    11. Yi Zheng & Li Liu & Victor Shi & Wenxing Huang & Jianxiu Liao, 2022. "A Resilience Analysis of a Medical Mask Supply Chain during the COVID-19 Pandemic: A Simulation Modeling Approach," IJERPH, MDPI, vol. 19(13), pages 1-21, June.
    12. Eva K. Lee & Siddhartha Maheshwary & Jacquelyn Mason & William Glisson, 2006. "Large-Scale Dispensing for Emergency Response to Bioterrorism and Infectious-Disease Outbreak," Interfaces, INFORMS, vol. 36(6), pages 591-607, December.
    13. Jinghong Wang & Siuming Lo & Qingsong Wang & Jinhua Sun & Honglin Mu, 2013. "Risk of Large‐Scale Evacuation Based on the Effectiveness of Rescue Strategies Under Different Crowd Densities," Risk Analysis, John Wiley & Sons, vol. 33(8), pages 1553-1563, August.
    14. Hu, Shao-Long & Han, Chuan-Feng & Meng, Ling-Peng, 2016. "Stochastic optimization for investment in facilities in emergency prevention," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 14-31.
    15. Ubaid Illahi & Mohammad Shafi Mir, 2021. "Maintaining efficient logistics and supply chain management operations during and after coronavirus (COVID-19) pandemic: learning from the past experiences," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 11157-11178, August.
    16. Arman Nedjati & Bela Vizvari & Gokhan Izbirak, 2016. "Post-earthquake response by small UAV helicopters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1669-1688, February.
    17. Xiaoyan Mu & Anthony Gar-On Yeh & Xiaohu Zhang, 2021. "The interplay of spatial spread of COVID-19 and human mobility in the urban system of China during the Chinese New Year," Environment and Planning B, , vol. 48(7), pages 1955-1971, September.
    18. Panayotis Christidis & Aris Christodoulou, 2020. "The Predictive Capacity of Air Travel Patterns during the Global Spread of the COVID-19 Pandemic: Risk, Uncertainty and Randomness," IJERPH, MDPI, vol. 17(10), pages 1-15, May.
    19. Mohammadi, Neda & Taylor, John E., 2017. "Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction," Applied Energy, Elsevier, vol. 195(C), pages 810-818.
    20. Lin, Yen-Hung & Batta, Rajan & Rogerson, Peter A. & Blatt, Alan & Flanigan, Marie, 2011. "A logistics model for emergency supply of critical items in the aftermath of a disaster," Socio-Economic Planning Sciences, Elsevier, vol. 45(4), pages 132-145, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:29:y:2009:i:4:p:438-460. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.