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

Annals of Operations Research volume 148, pages25–53(2006)Cite this article

Abstract

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.

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References

  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.).

  2. Altiok, T. (1997). Performance Analysis of Manufacturing Systems. Springer-Verlag, New York.

  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.

  4. Bellamy, R.J. and A.R. Freedman. (2001). “Bioterrorism.” QJM, 94(4), 227–334.

  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.

  6. Buzacott J.A. and J.G. Shanthikumar. (1993). Stochastic Models of Manufacturing Systems. Prentice Hall, New Jersey.

  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.”

  8. Centers for Disease Control and Prevention. (2002 November). Maxi-Vac 1.0, Beta Test Version.

  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).

  10. Clizbe, J.A. (2004). “Challenges in Managing Volunteers During Bioterrorism Response.” Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science, 2(4), 294–300.

  11. Dallery, Y. and Y. Frein. (1993). “On Decomposition Methods for Tandem Queueing Networks with Blocking.” Oper. Res., 41(2), 386–399.

  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.

  13. Eglese, R.W. (1990). “Simulated Annealing: A Tool for Operational Research.” European J. Oper. Res., 46,(3) 271–281.

  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.

  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=http://doi.acm.org/10.1145/508791.508819.

  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.

  17. Fauci, A.S. (2002). “Smallpox Vaccination Policy—The Need for Dialogue.” N. Engl. J. Med., 346(17), 1319–1320.

  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.

  19. Gani, R. and S. Leach. (2001). “Transmission Potential of Smallpox in Contemporary Populations.” Nature, 414(13).

  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.

  21. Giovachino, M.J. and B.G. McCue. (2003 April). “Mini-Vac Model Documentation.” Institute for Public Research, (IPR)10889.

  22. Gross, D. and C.M. Harris. (1998). Fundamentals of Queueing Theory. Wiley Interscience.

  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.

  24. Hopp, W.J. and M.L. Spearman. (2000). Factory Physics. McGraw-Hill/Irwin, New York (NY).

  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.

  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.

  27. Kaplan, E.H. (2004). “Preventing Second-Generation Infections in a Smallpox Bioterror Attack.” Epidemiology, 15(3), 264–270.

  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).

  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.

  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.

  31. Keim, M. and A.F. Kaufman. (1999). “Principles for Emergency Response to Bioterrorism.” Ann. Emerg. Med., 34(2), 177–182.

  32. Laguna, M. and R. Marti. (2003). Scatter Search: Methodology and Implementations in C. Kluwer Academic Publishers, Boston (MA).

  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).

  34. Larson, R.C. (2004). “Decision Models for Emergency Response Planning.” In D. Kamien (ed.), The McGraw-Hill Handbook of Homeland Security. McGraw-Hill.

  35. Law, A.M. and W.D. Kelton. (1991). Simulation Modeling and Analysis. McGrawhill, New York (NY).

  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.

  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.

  38. Mason, J. and M. Washington. (2003). “Optimizing Staff Allocation in Large-Scale Dispensing Centers.” CDC report.

  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.

  40. Nemhauser, G.L. and L.A. Wolsey. (1988). Integer and Combinatorial Optimization. Wiley, New York.

  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.

  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.

  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.

  44. Papadopoulos, H.T., C. Heavey, and J. Browne. (1993). Queueing Theory in Manufacturing Systems Analysis and Design. Chapman and Hall, London.

  45. RealOpt User Manual, Georgia Institute of Technology, 2004–2006.

  46. Reis, B.Y. and K.D. Mandl. (2003). “Time Series Modeling for Syndromic Surveillance.” BMC Medical Informatics and Decision Making, 3(2).

  47. Ross, S.M. (1995). Stochastic Processes. John Wiley and Sons.

  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.

  49. Scott, S. and C. Duncan. (2001). Biology of Plagues: Evidence from Historical Populations. Cambridge University Press, Cambridge.

  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.

  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.

  52. Spinellis, D. and C. Papadopoulos. (2000b). “Stochastic Algorithms for Buffer Allocation in Reliable Production Lines.” Mathematical Problems in Engineering, 5(6), 441–458.

  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.

  54. Viswanadham, N. and Y. Narahari. (1992). Performance Modeling of Automated Manufacturing Systems. Prentice Hall, New Jersey.

  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.

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Affiliations

  1. School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia
    • Eva K. Lee
    •  & Siddhartha Maheshwary
  2. Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia
    • Eva K. Lee
  3. Public Health Environmental Readiness Branch, Centers for Disease Control and Prevention, Atlanta, USA
    • Jacquelyn Mason
  4. DeKalb County Board of Health, Office of Emergency Preparedness, Atlanta, Georgia
    • William Glisson
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  1. Eva K. Lee
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  2. Siddhartha Maheshwary
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  4. William Glisson
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Corresponding author

Correspondence to Eva K. Lee.

Additional information

Preliminary results of this study were presented at INFORMS Colorado Oct. 2004. The system developed has been reported in the Department of Homeland Security IAIP Directorate Daily Open Source Infrastructure Report, “New Computer Program helps Health Departments Halt Outbreaks,” Sept. 2004; and Bioscience Innovations, “Emergency treatment response for bioterrorism and infectious disease outbreak,” Oct. 2004.

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Lee, E.K., Maheshwary, S., Mason, J. et al. Decision support system for mass dispensing of medications for infectious disease outbreaks and bioterrorist attacks. Ann Oper Res 148, 25–53 (2006). https://doi.org/10.1007/s10479-006-0087-7

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Keywords

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