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OR Modeling and AIDS Policy: From Theory to Practice

Author

Listed:
  • James G. Kahn

    (Center for AIDS Prevention Studies, Institute for Health Policy Studies, University of California, San Francisco, San Francisco, California 94143)

  • Margaret L. Brandeau

    (Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305)

  • John Dunn-Mortimer

    (Consultant on Health Care Systems and Government Affairs, 715 Laurel Avenue, #201, San Mateo, California 94401)

Abstract

The AIDS epidemic is a serious, growing public health problem worldwide, but resources for treating HIV-infected patients and for combating the spread of the virus are limited. Governments, public-health agencies, and health-care providers must determine how best to allocate scarce resources for HIV treatment and prevention among different programs and populations. OR-based models have influenced—and can influence—AIDS policy decisions. Mathematical modeling has had an effect on AIDS policy in a number of areas, including estimating HIV prevalence and incidence in the United States, understanding the pathophysiology of HIV, evaluating costs and benefits of HIV-screening programs, evaluating the effects of needle-exchange programs, and determining policies for HIV/AIDS care in California. Further work is needed to model a range of programs using comparable methods, to model overall epidemic control strategy, and to improve the usefulness of OR-based models for policy making.

Suggested Citation

  • James G. Kahn & Margaret L. Brandeau & John Dunn-Mortimer, 1998. "OR Modeling and AIDS Policy: From Theory to Practice," Interfaces, INFORMS, vol. 28(3), pages 3-22, June.
    • Handle: RePEc:inm:orinte:v:28:y:1998:i:3:p:3-22
    DOI: 10.1287/inte.28.3.3
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    References listed on IDEAS

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    Cited by:

    1. Richter, Anke & Hicks, Katherine A. & Earnshaw, Stephanie R. & Honeycutt, Amanda A., 2008. "Allocating HIV prevention resources: A tool for state and local decision making," Health Policy, Elsevier, vol. 87(3), pages 342-349, September.
    2. Stephen E. Chick & Sada Soorapanth & James S. Koopman, 2003. "Inferring Infection Transmission Parameters That Influence Water Treatment Decisions," Management Science, INFORMS, vol. 49(7), pages 920-935, July.
    3. Sarang Deo & Kumar Rajaram & Sandeep Rath & Uday S. Karmarkar & Matthew B. Goetz, 2015. "Planning for HIV Screening, Testing, and Care at the Veterans Health Administration," Operations Research, INFORMS, vol. 63(2), pages 287-304, April.
    4. David W. Hutton & Margaret L. Brandeau & Samuel K. So, 2011. "Doing Good with Good OR: Supporting Cost-Effective Hepatitis B Interventions," Interfaces, INFORMS, vol. 41(3), pages 289-300, June.
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    6. Jing Yao & Alan T. Murray, 2014. "Locational Effectiveness of Clinics Providing Sexual and Reproductive Health Services to Women in Rural Mozambique," International Regional Science Review, , vol. 37(2), pages 172-193, April.
    7. Lasry, Arielle & Zaric, Gregory S. & Carter, Michael W., 2007. "Multi-level resource allocation for HIV prevention: A model for developing countries," European Journal of Operational Research, Elsevier, vol. 180(2), pages 786-799, July.

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