Public Health Modeling at the Centers for Disease Control and Prevention

  • Arielle Lasry
  • Michael L. Washington
  • Hannah K. Smalley
  • Faramroze Engineer
  • Pinar Keskinocak
  • Larry Pickering
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 190)


At the Centers for Disease Control and Prevention, there is a growing interest in promoting the use of mathematical modeling to support public health policies. This chapter presents three examples of operations research models developed and employed by the Centers for Disease Control and Prevention. First, we discuss the Adult Immunization Scheduler, which uses dynamic programming methods to establish a personalized vaccination schedule for adults aged 19 and older. The second operations research project is a discrete event simulation model used to estimate the throughput and budget for mass vaccination clinics during the 2009–2010 H1N1 pandemic. Lastly, we describe a national HIV resource allocation model that uses nonlinear programming methods to optimize the allocation of funds to HIV prevention programs and populations.


Population Subgroup Injection Drug User H1N1 Pandemic Immunization Schedule Arrival Intensity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Centers for Disease Control and Prevention (2010) Recommended adult immunization schedule—United States. Accessed 15 Nov 2010
  2. 2.
    National Center for Health Statistics—United States (2007 (Revised 2008)) Vaccination coverage among U.S. adults, National Immunization Survey—Adult. Accessed 15 Nov 2010
  3. 3.
    California Department of Public Health (2010) Pertussis report. p 5Google Scholar
  4. 4.
    Wendelboe A et al. (2007) Transmission of Bordetella pertussis to young infants. Pediatr Infect Dis J 26(4):293–299CrossRefGoogle Scholar
  5. 5.
    Centers for Disease Control and Prevention (2010) Catch-up Immunization Scheduler for children six years of age and younger. Accessed 15 Nov 2010
  6. 6.
    Engineer FG, Keskinocak P, Pickering LK (2009) OR practice—catch-up scheduling for childhood vaccination. Oper Res 57(6):1307–1319CrossRefGoogle Scholar
  7. 7.
    Smalley HK et al. (2011) Universal tool for vaccine scheduling—applications for children and adults. Interfaces 41(5):436–454Google Scholar
  8. 8.
    Cho B-H et al. (2011) A tool for the economic analysis of mass prophylaxis operations with an application to H1N1 influenza vaccination clinics. J Public Health Manag Pract 17(1):E22–E28Google Scholar
  9. 9.
    Washington ML (2009) Evaluating the capability and cost of a mass influenza and pneumococcal vaccination clinic via computer simulation. Med Decis Making 29(4):414–423CrossRefGoogle Scholar
  10. 10.
    Luenberger DG (1979) Introduction to dynamic systems: theory, models, and applications. Wiley, New York, p 446Google Scholar
  11. 11.
    Zaric GS et al. (1998) The effect of protease inhibitors on the spread of HIV and the development of drug resistance: a simulation study. Simulation 71:262–275CrossRefGoogle Scholar
  12. 12.
    Marks G et al. (2005) Meta-analysis of high-risk sexual behavior in persons aware and unaware they are infected with HIV in the United States: implications for HIV prevention programs. J Acquir Immune Defic Syndr 39:446–453CrossRefGoogle Scholar
  13. 13.
    Weinhardt LS et al. (1999) Effects of HIV counseling and testing on sexual risk behavior: a meta-analytic review of published research, 1985–1997. Am J Public Health 89(9):1397–1405CrossRefGoogle Scholar
  14. 14.
    Marks G et al. (2009) Understanding differences in HIV sexual transmission among Latino and black men who have sex with men: the Brothers y Hermanos study. AIDS Behav 13(4):682–690CrossRefGoogle Scholar
  15. 15.
    Lasry A et al. (2011) A model for allocating CDC’s HIV prevention resources in the United States. Health Care Manag Sci 14(1):115–124CrossRefGoogle Scholar
  16. 16.
    Lasry A et al. (2012) Allocating HIV prevention funds in the United States: recommendations from an optimization model. PLoS ONE 7(6):e37545CrossRefGoogle Scholar
  17. 17.
    Schackman BR et al. (2006) The lifetime cost of current human immunodeficiency virus care in the United States. Med Care 44(11):990–997CrossRefGoogle Scholar
  18. 18.
    The White House Office of National AIDS Policy (2010) National HIV/AIDS strategy for the United States. Washington, DC. p 60Google Scholar
  19. 19.
    Mooney G (1998) “Communitarian claims” as an ethical basis for allocating health care resources. Soc Sci Med 47(9):1171–1180CrossRefGoogle Scholar
  20. 20.
    Kahn JG, Marseille E (2002) A saga in international HIV policy modeling: preventing mother-to-child HIV transmission. J Policy Anal Manag 21(3):499–505CrossRefGoogle Scholar
  21. 21.
    McGregor M (2006) What decision-makers want and what they have been getting. Value Health 9(3):181–185CrossRefGoogle Scholar
  22. 22.
    Lasry A, Carter MW, Zaric GS (2011) Allocating funds for HIV/AIDS: a descriptive study of KwaDukuza, South Africa. Health Policy Plan 26:33CrossRefGoogle Scholar
  23. 23.
    Lasry A, Richter A, Lutscher F (2009) Recommendations for increasing the use of HIV/AIDS resource allocation models. BMC Public Health 9(Suppl 1):S8CrossRefGoogle Scholar
  24. 24.
    Keeney RL (1988) Structuring objectives for problems of public interest. Oper Res 36(3):396–405CrossRefGoogle Scholar
  25. 25.
    Pinkerton SD et al. (2002) Ethical issues in cost-effectiveness analysis. Eval Program Plann 25:71–83CrossRefGoogle Scholar
  26. 26.
    Granata AV, Hillman AL (1998) Competing practice guidelines: using cost-effectiveness analysis to make optimal decisions. Ann Intern Med 128(1):56–63Google Scholar
  27. 27.
    Jackson T (1996) Health economics and policy: ethical dilemmas in the science of scarcity. In: Daly J (ed) Ethical intersections: health research, methods, and researcher responsibility. Westview Press, Boulder, CO, p 127–138Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Arielle Lasry
    • 1
  • Michael L. Washington
    • 2
  • Hannah K. Smalley
    • 3
  • Faramroze Engineer
    • 4
  • Pinar Keskinocak
    • 3
  • Larry Pickering
    • 5
  1. 1.Division of HIV/AIDS PreventionCenters for Disease Control and PreventionAtlantaUSA
  2. 2.Preparedness Modeling UnitCenters for Disease Control and PreventionAtlantaUSA
  3. 3.H. Milton Stewart School of Industrial and Systems EngineeringGeorgia Institute of TechnologyAtlantaUSA
  4. 4.School of Mathematical & Physical SciencesUniversity of NewcastleOurimbahAustralia
  5. 5.National Center for Immunization and Respiratory Diseases, Centers for Disease Control and PreventionAtlantaUSA

Personalised recommendations