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Modeling the Public Health Response to Bioterrorism: Using Discrete Event Simulation to Design Antibiotic Distribution Centers

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  • Nathaniel Hupert
  • Alvin I. Mushlin
  • Mark A. Callahan

Abstract

Background Post-exposure prophylaxis is a critical component of the public health response to bioterrorism. Computer simulation modeling may assist in designing antibiotic distribution centers for this task. Methods The authors used discrete event simulation modeling to determine staffing levels for entry screening, triage, medical evaluation, and drug dispensing stations in a hypothetical antibiotic distribution center operating in low, medium, and high disease prevalence bioterrorism response scenarios. Patient arrival rates and processing times were based on prior mass prophylaxis campaigns. Multiple sensitivity analyses examined the relationship between average staff utilization rate (UR) (i.e., percentage of time occupied in patient contact) and capacity of the model to handle surge arrivals. Results Distribution center operation required from 93 staff for the low-prevalence scenario to 111 staff for the high-prevalence scenario to process approximately 1000 people per hour within the baseline model assumptions. Excess capacity to process surge arrivals approximated (1-UR) for triage staffing. Conclusions Discrete event simulation modeling is a useful tool in developing the public health infrastructure for bioterrorism response. Live exercises to validate the assumptions and outcomes presented here may improve preparedness to respond to bioterrorism.

Suggested Citation

  • Nathaniel Hupert & Alvin I. Mushlin & Mark A. Callahan, 2002. "Modeling the Public Health Response to Bioterrorism: Using Discrete Event Simulation to Design Antibiotic Distribution Centers," Medical Decision Making, , vol. 22(1_suppl), pages 17-25, September.
    • Handle: RePEc:sae:medema:v:22:y:2002:i:1_suppl:p:17-25
    DOI: 10.1177/027298902237709
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    References listed on IDEAS

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    1. Wetter, D.C. & Daniell, W.E. & Treser, C.D., 2001. "Hospital preparedness for victims of chemical or biological terrorism," American Journal of Public Health, American Public Health Association, vol. 91(5), pages 710-716.
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    Cited by:

    1. Ali Asgary & Svetozar Zarko Valtchev & Michael Chen & Mahdi M. Najafabadi & Jianhong Wu, 2020. "Artificial Intelligence Model of Drive-Through Vaccination Simulation," IJERPH, MDPI, vol. 18(1), pages 1-10, December.
    2. Gregory S. Zaric & Dena M. Bravata & Jon-Erik Cleophas Holty & Kathryn M. McDonald & Douglas K. Owens & Margaret L. Brandeau, 2008. "Modeling the Logistics of Response to Anthrax Bioterrorism," Medical Decision Making, , vol. 28(3), pages 332-350, May.
    3. P. Daniel Wright & Matthew J. Liberatore & Robert L. Nydick, 2006. "A Survey of Operations Research Models and Applications in Homeland Security," Interfaces, INFORMS, vol. 36(6), pages 514-529, December.
    4. 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.
    5. Joseph R. Egan & Richard Amlôt, 2012. "Modelling Mass Casualty Decontamination Systems Informed by Field Exercise Data," IJERPH, MDPI, vol. 9(10), pages 1-26, October.
    6. 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.
    7. Muckstadt, John A. & Klein, Michael G. & Jackson, Peter L. & Gougelet, Robert M. & Hupert, Nathaniel, 2023. "Efficient and effective large-scale vaccine distribution," International Journal of Production Economics, Elsevier, vol. 262(C).
    8. 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.
    9. Ramwadhdoebe, Sabrina & Buskens, Erik & Sakkers, Ralph J.B. & Stahl, James E., 2009. "A tutorial on discrete-event simulation for health policy design and decision making: Optimizing pediatric ultrasound screening for hip dysplasia as an illustration," Health Policy, Elsevier, vol. 93(2-3), pages 143-150, December.
    10. Francesco Pilati & Riccardo Tronconi & Giandomenico Nollo & Sunderesh S. Heragu & Florian Zerzer, 2021. "Digital Twin of COVID-19 Mass Vaccination Centers," Sustainability, MDPI, vol. 13(13), pages 1-26, July.
    11. Peter Williams & Guangfu Tai & Yiming Lei, 2010. "Simulation based analysis of patient arrival to health care systems and evaluation of an operations improvement scheme," Annals of Operations Research, Springer, vol. 178(1), pages 263-279, July.
    12. 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.
    13. David L. Craft & Lawrence M. Wein & Alexander H. Wilkins, 2005. "Analyzing Bioterror Response Logistics: The Case of Anthrax," Management Science, INFORMS, vol. 51(5), pages 679-694, May.
    14. Douglas K. Owens, 2002. "Analytic Tools for Public Health Decision Making," Medical Decision Making, , vol. 22(1_suppl), pages 3-10, September.

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