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Large-Scale Dispensing for Emergency Response to Bioterrorism and Infectious-Disease Outbreak

Author

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  • Eva K. Lee

    (Center for Operations Research in Medicine and HealthCare, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0205)

  • Siddhartha Maheshwary

    (Center for Operations Research in Medicine and HealthCare, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0205)

  • Jacquelyn Mason

    (Public Health Environmental Readiness Branch, Centers for Disease Control and Prevention, Division of Emergency and Environmental Health Services, Chamblee, Georgia 30341)

  • William Glisson

    (DeKalb County Board of Health, Office of Emergency Preparedness, DeKalb, Georgia 30030)

Abstract

We describe RealOpt © , a simulation and decision-support system for planning large-scale emergency dispensing clinics to respond to biological threats and infectious-disease outbreaks. The system allows public-health administrators to investigate clinic-design and staffing scenarios quickly. The system incorporates efficient optimization technology seamlessly interfaced with a simulation module.The simulation studies we present explore facility-layout and staffing scenarios for an actual anthrax-emergency drill, and we discuss post-event analysis. Using our staff allocation and assignments for the exercise, DeKalb County achieved the highest throughput among all counties that simultaneously conducted the same scale of anthrax drill at various locations. Its labor usage was at or below that of the other counties. The external evaluators commented that DeKalb produced the most efficient floor plan (with no path crossing), the most cost-effective dispensing (lowest labor and throughput value), and the smoothest operations (shortest average wait time, average queue length, and equalized utilization rate). The study proves that even without historical data, the use of our system enables emergency personnel to plan ahead and be able to estimate required labor resources accurately. The exercise also revealed many areas that need attention during the operations planning and design of dispensing centers.A real-time decision-support system is, therefore, viable through careful design of a stand-alone simulator, coupled with powerful and tailored optimization solvers. The system facilitates analysis of “what-if” scenarios, and serves as an invaluable tool for operational planning and dynamic, on-the-fly reconfigurations of large-scale emergency dispensing clinics. It also allows performing “virtual field exercises” on the decision-support system, offering insight into operations flow and bottlenecks when mass dispensing is required for a region with a large population. Working with emergency-response departments, we will perform additional tuning and development of the system to address different biological attacks and infectious-disease outbreaks, and to ensure its practicality and usability.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orinte:v:36:y:2006:i:6:p:591-607
    DOI: 10.1287/inte.1060.0257
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    References listed on IDEAS

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    8. Zhao, Laijun & Wang, Qin & Cheng, Jingjing & Zhang, Ding & Ma, Ting & Chen, Yucheng & Wang, Jiajia, 2012. "The impact of authorities’ media and rumor dissemination on the evolution of emergency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 3978-3987.
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    10. Aakil M. Caunhye & Xiaofeng Nie, 2018. "A Stochastic Programming Model for Casualty Response Planning During Catastrophic Health Events," Transportation Science, INFORMS, vol. 52(2), pages 437-453, March.
    11. Eva K. Lee & Chien-Hung Chen & Ferdinand Pietz & Bernard Benecke, 2009. "Modeling and Optimizing the Public-Health Infrastructure for Emergency Response," Interfaces, INFORMS, vol. 39(5), pages 476-490, October.
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    19. Feng, Keli & Bizimana, Emmanuel & Agu, Deedee D. & Issac, Tana T., 2012. "Optimization and Simulation Modeling of Disaster Relief Supply Chain: A Literature Review," MPRA Paper 58204, University Library of Munich, Germany.

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