Annals of Operations Research

, Volume 148, Issue 1, pp 25–53 | Cite as

Decision support system for mass dispensing of medications for infectious disease outbreaks and bioterrorist attacks

  • Eva K. LeeEmail author
  • Siddhartha Maheshwary
  • Jacquelyn Mason
  • William Glisson


A simulation and decision support system, RealOpt©, for planning large-scale emergency dispensing clinics to respond to biological threats and infectious disease outbreaks is described. The system allows public health administrators to investigate clinic design and staffing scenarios quickly.

RealOpt© incorporates efficient optimization technology seamlessly interfaced with a simulation module. The system's correctness and computational advantage are validated via comparisons against simulation runs of the same model developed on a commercial system. Simulation studies to explore facility layout and staffing scenarios for smallpox vaccination and for an actual anthrax-treatment dispensing exercise and post event analysis are presented.

The system produces results consistent with the model built on the commercial system, but requires only a fraction of the computational time. Each smallpox scenario runs within 1 CPU minute on RealOpt©, versus run times of over 5–10 h on the commercial system. The system's fast computational time enables its use in large-scale studies, in particular an anthrax response planning exercise involving a county with 864,000 households. The computational effort required for this exercise was roughly 30 min for all scenarios considered, demonstrating that RealOpt© offers a very promising avenue for pursuing a comprehensive investigation involving a more diverse set of scenarios, and justifying work towards development of a robust system that can be widely deployed for use by state, local, and tribal health practitioners.

Using our staff allocation and assignments for the Anthrax field exercise, DeKalb county achieved the highest throughput among all counties that simultaneously conducted the same scale of Anthrax exercise at various locations, with labor usage at or below the other counties. Indeed, DeKalb exceeded the targeted number of households, and it processed 50% more individuals compared to the second place county. None of the other counties achieved the targeted number of households. The external evaluators commented that DeKalb produced the most efficient floor plan (with no path crossing), the most cost-effective dispensing (lowest labor/throughput value), and the smoothest operations (shortest average wait time, average queue length, equalized utilization rate). The study proves that even without historical data, using our system one can plan ahead and be able to wisely estimate the required labor resources.

The exercise also revealed many areas that need attention during the operations planning and design of dispensing centers. The type of disaster being confronted (e.g., biological attack, infectious disease outbreak, or a natural disaster) also dictates different design considerations with respect to the dispensing clinic, facility locations, dispensing and backup strategies, and level of security protection. Depending on the situation, backup plans will be different, and the level of security and military personnel, as well as the number of healthcare workers required, will vary.

In summary, the study shows that a real-time decision support system is viable through careful design of a stand-alone simulator coupled with powerful tailor-designed optimization solvers. The flexibility of performing empirical tests quickly means the system is amenable for use in training and preparation, and for strategic planning before and during an emergency situation. 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 for “virtual field exercises” to be performed on the decision support system, offering insight into operations flow and bottlenecks when mass dispensing is required for a region with a large population. The system, designed in modular form with a flexible implementation, enables future expansion and modification regarding emergency center design with respect to treatment for different biological threats or disease outbreaks. Working with emergency response departments, further fine-tuning and development of the system will be made to address different biological attacks and infectious disease outbreaks, and to ensure its practicality and usability.


Bioterrorism Infectious disease Decision support system Simulation Optimization Anthrax Smallpox Emergency response Resource allocation 


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  1. Alibek, K. (1999). Biohazard: The Chilling True Story of the Largest Covert Biological Weapons Program in the World—Told from Inside by the Man Who Ran It. Random House, New York (N.Y.).Google Scholar
  2. Altiok, T. (1997). Performance Analysis of Manufacturing Systems. Springer-Verlag, New York.Google Scholar
  3. Barnett, D.J., R.D. Balicer, D. Blodgett, A.L. Fews, C.L. Parker, and J.M. Links. (2005). “The Application of the Haddon Matrix to Public Health Readiness and Response Planning.” Environmental Health Perspectives, 113(5),561–566.Google Scholar
  4. Bellamy, R.J. and A.R. Freedman. (2001). “Bioterrorism.” QJM, 94(4), 227–334.CrossRefGoogle Scholar
  5. Bravata, D.M., K.M. McDonald, W.M. Smith, C. Rydzak, H. Szeto, D.L. Buckeridge, C. Haberland, and D.K. Owens. (2004). “Systematic Review: Surveillance Systems for Early Detection of Bioterrorism-Related Diseases.” Annals of Internal Medicine, 140(11), 910–922.Google Scholar
  6. Buzacott J.A. and J.G. Shanthikumar. (1993). Stochastic Models of Manufacturing Systems. Prentice Hall, New Jersey.Google Scholar
  7. Centers for Disease Control. (2002 August). “Smallpox Vaccination Clinic Guide: Logistical Considerations and Guidance for State and Local Planning for Emergency Large-Scale, Post-Event, Voluntary Administration of Smallpox Vaccine.”Google Scholar
  8. Centers for Disease Control and Prevention. (2002 November). Maxi-Vac 1.0, Beta Test Version.Google Scholar
  9. Centers for Disease Control and Prevention. (2002). “Evaluation of Post Exposure Antibiotic Prophylaxis to Prevent Prevent Anthrax.” Morbidity and Mortality Weekly Report, 51(03).Google Scholar
  10. Clizbe, J.A. (2004). “Challenges in Managing Volunteers During Bioterrorism Response.” Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science, 2(4), 294–300.CrossRefGoogle Scholar
  11. Dallery, Y. and Y. Frein. (1993). “On Decomposition Methods for Tandem Queueing Networks with Blocking.” Oper. Res., 41(2), 386–399.Google Scholar
  12. Drenkard, K., G. Rigotti, D. Hanfling, T.L. Fahlgren, and G. LaFrancois. (2002). “Healthcare System Disaster Preparedness, Part 1: Readiness Planning.” Journal of Nursing Administration, 32(9),461–469.Google Scholar
  13. Eglese, R.W. (1990). “Simulated Annealing: A Tool for Operational Research.” European J. Oper. Res., 46,(3) 271–281.CrossRefGoogle Scholar
  14. Epstein, J., D.A.T. Cummings, S. Chakravarty, R. Singa, and D.S. Burke. (2002). “Toward a Containment Strategy for Smallpox Bioterror: An Individual-Based Computational Aapproach [working paper no. 31].” Brookings Institution, Johns Hopkins University Center on Social and Economic Dynamics.Google Scholar
  15. Eubank, S. (2002). “Scalable, Efficient Epidemiological Simulation.” In Proceedings of the 2002 ACM Symposium on Applied Computing (Madrid, Spain, March 11—14, 2002). SAC ’02. ACM Press, New York, NY, 139–145. DOI= Scholar
  16. Fast, J.D., B.L. O’Steen, and R.P. Addis. (1995). “Advanced Atmospheric Modeling for Emergency Response.” Journal of Applied Meteorology, 34(3), 626–649.CrossRefGoogle Scholar
  17. Fauci, A.S. (2002). “Smallpox Vaccination Policy—The Need for Dialogue.” N. Engl. J. Med., 346(17), 1319–1320.CrossRefGoogle Scholar
  18. Franz, D.R., P.B. Jahrling, A.M. Friedlander, D.J. McClain, D.L. Hoover, W.R. Bryne, J.A. Pavlin, G.W. Christopher, and E.M. Eitzen Jr. (1997). “Clinical Recognition and Management of Patients Exposed to Biological Warfare Agents.” JAMA, 278(5), 399–411.CrossRefGoogle Scholar
  19. Gani, R. and S. Leach. (2001). “Transmission Potential of Smallpox in Contemporary Populations.” Nature, 414(13).Google Scholar
  20. Gershwin, S.B. (1987). “An Efficient Decomposition Method for the Approximate Evaluation of Tandem Queues with Finite Storage Space and Blocking.” Oper. Res., 35(2), 291–305.CrossRefGoogle Scholar
  21. Giovachino, M.J. and B.G. McCue. (2003 April). “Mini-Vac Model Documentation.” Institute for Public Research, (IPR)10889.Google Scholar
  22. Gross, D. and C.M. Harris. (1998). Fundamentals of Queueing Theory. Wiley Interscience.Google Scholar
  23. Heavey, C., H.T. Papadopoulos, and J. Browne. (1993). “The Throughput Rate of Multistation Unreliable Production Lines.” European J. Oper. Res., 68,(1) 69–89.CrossRefGoogle Scholar
  24. Hopp, W.J. and M.L. Spearman. (2000). Factory Physics. McGraw-Hill/Irwin, New York (NY).Google Scholar
  25. Hupert, N., G.M.L. Bearman, A.I. Mushlin, and M.A. Callahan. (2003). “Accuracy of Screening for Inhalational Anthrax after a Bioterrorist Aattack.” Ann. Int. Med., 139(5), 337–345.Google Scholar
  26. Hupert, N., A.J. Mushlin, and M.A. Callahan. (2002). “Modeling the Public Health Response to Bioterrorism: Using Discrete Event Simulation to Design Antibiotic Distribution Centers.” Medical Decision Making, 22 (Suppl), S17–S25.Google Scholar
  27. Kaplan, E.H. (2004). “Preventing Second-Generation Infections in a Smallpox Bioterror Attack.” Epidemiology, 15(3), 264–270.CrossRefGoogle Scholar
  28. Kaplan, E.H., D.L. Craft, and L.M. Wein. (2002). “Emergency Response to a Smallpox Attack: The Case for Mass Vaccination.” Proceedings of the National Academy of Sciences, 99(16).Google Scholar
  29. Kaplan, E.H., D.L. Craft, and L.M. Wein. (2003). “Analyzing Bioterror Response Logistics: The Case of Smallpox.” Mathematical Biosciences, 185 (1), 33–72.CrossRefGoogle Scholar
  30. Kaufmann, A.F., M.T. Meltzer, and G.P. Schmid. (1997). “The Economic Impact of a Bioterrorist Attack: Are Prevention and Postattack Intervention Programs Justifiable?” Emerg. Infect. Dis., 3(2), 83–94.CrossRefGoogle Scholar
  31. Keim, M. and A.F. Kaufman. (1999). “Principles for Emergency Response to Bioterrorism.” Ann. Emerg. Med., 34(2), 177–182.CrossRefGoogle Scholar
  32. Laguna, M. and R. Marti. (2003). Scatter Search: Methodology and Implementations in C. Kluwer Academic Publishers, Boston (MA).Google Scholar
  33. Larkin, G.L. and J. Arnold. (2004). “Ethical Considerations in Emergency Planning, Preparedness, and Response to Acts of Terrorism.” Prehospital and Disaster Medicine, 18(3).Google Scholar
  34. Larson, R.C. (2004). “Decision Models for Emergency Response Planning.” In D. Kamien (ed.), The McGraw-Hill Handbook of Homeland Security. McGraw-Hill.Google Scholar
  35. Law, A.M. and W.D. Kelton. (1991). Simulation Modeling and Analysis. McGrawhill, New York (NY).Google Scholar
  36. Lee, E.K. and S. Maheshwary. (2004). Systems and Methods for Emergency Treatment Response and Real-Time Staff Allocation for Bioterrorism and Infectious Disease Outbreak [copyright 2004, provisional patent June 2004, 2005]. Georgia Institute of Technology.Google Scholar
  37. Lee, E.K., S. Maheshwary, and J. Mason. (2005). “Real-Time Staff Allocation for Emergency Treatment Response of Biological Threats and Infectious Disease Outbreak.” Medical Decision Making. Competitive selected as INFORMS William Pierskalla Best Paper Award on research excellence in HealthCare and Management Science, November 2005.Google Scholar
  38. Mason, J. and M. Washington. (2003). “Optimizing Staff Allocation in Large-Scale Dispensing Centers.” CDC report.Google Scholar
  39. Meltzer, M., I. Damon, J.W. LeDunc, and D.J. Millar. (2001). “Modeling Potential Responses to Smallpox as a Bioterrorist Weapon.” Emerg. Infect. Dis., 7(6),959–969.Google Scholar
  40. Nemhauser, G.L. and L.A. Wolsey. (1988). Integer and Combinatorial Optimization. Wiley, New York.Google Scholar
  41. Nguyen, S., J.M. Rosen, and C.E. Koop. (2005). “Medicine Meets Virtual Reality 13: The Magical Next Becomes the Medical Now.” Emerging Technologies for Bioweapons Defense, Amsterdam, IOS Press, The Netherlands Vol. 111, 356–361.Google Scholar
  42. Noji, E.K. (2003). “Medical Preparedness and Response to Terrorism with Biological and Chemical Agents–Present Status in USA.” International Journal of Disaster Medicine, 1(1), 51–55.CrossRefGoogle Scholar
  43. Okumura T., K. Suzuki, A. Fukuda, A. Kohama, N. Takasu, S. Ishimatsu, and S. Hinohara. (1998). “The Tokyo Subway Sarin Attack: Disaster Management, Part 1: Community Emergency Response.” Academic Emergency Medicine, 5, 613–617.CrossRefGoogle Scholar
  44. Papadopoulos, H.T., C. Heavey, and J. Browne. (1993). Queueing Theory in Manufacturing Systems Analysis and Design. Chapman and Hall, London.Google Scholar
  45. RealOpt User Manual, Georgia Institute of Technology, 2004–2006.Google Scholar
  46. Reis, B.Y. and K.D. Mandl. (2003). “Time Series Modeling for Syndromic Surveillance.” BMC Medical Informatics and Decision Making, 3(2).Google Scholar
  47. Ross, S.M. (1995). Stochastic Processes. John Wiley and Sons.Google Scholar
  48. Rotz, L.D. and J.M. Hughes. (2004). “Advances in Detecting and Responding to Threats from Bioterrorism and Emerging Infectious Disease.” Nature Medicine, 10(12 Suppl), S130–S136.Google Scholar
  49. Scott, S. and C. Duncan. (2001). Biology of Plagues: Evidence from Historical Populations. Cambridge University Press, Cambridge.Google Scholar
  50. Sidel, V.W., R.M. Gould, and H.W. Cohen. (2002). “Bioterrorism Preparedness: Cooptation of Public Health.” Medicine & Global Survival, 7(2),82–89.Google Scholar
  51. Spinellis, D. and C.T. Papadopoulos. (2000a). “A Simulated Annealing Approach for Buffer Allocation in Reliable Production Lines.” Ann. of Oper. Res., 93(1–4), 373–384.Google Scholar
  52. Spinellis, D. and C. Papadopoulos. (2000b). “Stochastic Algorithms for Buffer Allocation in Reliable Production Lines.” Mathematical Problems in Engineering, 5(6), 441–458.Google Scholar
  53. Tompkins, G. and F. Azadivar. (1995). “Genetic Algorithms in Optimizing Simulated Systems.” In Proceedings of the 27th Conference on Winter Simulation, Arlington (VA), ACM Press, New York (NY), 757–762.Google Scholar
  54. Viswanadham, N. and Y. Narahari. (1992). Performance Modeling of Automated Manufacturing Systems. Prentice Hall, New Jersey.Google Scholar
  55. Wein, L.M., D.L. Craft, and E.H. Kaplan. (2003). “Emergency Response to an Anthrax Attack.” In Proceedings of the National Academy of Sciences, 100(7), 4346–4351.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Eva K. Lee
    • 1
    • 2
    Email author
  • Siddhartha Maheshwary
    • 1
  • Jacquelyn Mason
    • 3
  • William Glisson
    • 4
  1. 1.School of Industrial and Systems EngineeringGeorgia Institute of TechnologyAtlantaGeorgia
  2. 2.Winship Cancer InstituteEmory University School of MedicineAtlantaGeorgia
  3. 3.Public Health Environmental Readiness BranchCenters for Disease Control and PreventionAtlantaUSA
  4. 4.DeKalb County Board of HealthOffice of Emergency PreparednessAtlantaGeorgia

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