Elisa Long

Profile photo of Elisa Long
“Every decision-maker in health care, whether a patient, provider or policymaker, must balance difficult choices with unknowable results. The best any of us can do is collect as much intelligence as possible and make informed decisions.”

Assistant Professor of Decisions, Operations, and Technology Management

(310) 825-4458

Areas of Expertise

  • Decision-Analytic Modeling
  • Dynamic Systems
  • Health Care Operations
  • Public Health Applications
  • Queuing in Hospital Settings
  • Resource Allocation
  • Simulation



Assistant Professor Elisa Long’s research integrates epidemiological modeling, economic analysis and decision making under uncertainty, with the aim of assessing the value of health interventions to help policymakers allocate limited resources most effectively. She has constructed mathematical models to simulate HIV epidemics in Russia, India, South Africa, Ghana and the United States, with the goal of identifying what combination of investments maximizes “bang for the buck.”

Long’s research on the cost-effectiveness of HIV screening was cited by the U.S. Centers for Disease Control and Prevention in their revised recommendations for increased screening of high-risk individuals. She recently published a study that optimizes resource allocation for emerging epidemics, and examined the 2014-2015 Ebola outbreak in West Africa to determine which regions should receive treatment priority (to appear in Manufacturing & Service Operations Management).

While pursuing her Ph.D. in management science and engineering at Stanford, Long became interested in applying quantitative methodologies in operations research to important policy questions in health care. She has published prolifically in business and medical journals on topics in health policy modeling, hospital operations management, and medical technology cost-effectiveness. Her first paper on breast cancer, published in JAMA Oncology, examined the controversial question of genetic testing for breast cancer among all women, not just those with known family history. Given that only 1 in 400 women carry a BRCA mutation, at a price of $4,000, universal testing is not a cost-effective use of resources, and poses additional challenges in terms of feasibility.  In a related study, published in Decision Analysis, Long and her collaborators built a decision analytic model to show when BRCA mutation carriers should optimally undergo surgery. For this new area of research, she received the 2015 UCLA Faculty Career Development Award.

At Anderson, Long teaches the introductory Data and Decisions course for MBA and EMBA students. Her goal in the classroom is to distill information for students in the most relevant possible way, “whether it’s reading a newspaper article with a different perspective, or creating a model to help decide whether to buy or lease a new car,” she says. She uses the classic example of Let’s Make a Deal to demonstrate that probability is a field in which your intuition can often lead you astray. “You must take into account your prior state of beliefs and what new information is presented, before calculating the probability of observing some outcome. This is as true for a game show as for interpreting a genetic test result.” What MBA candidates learn from television game show strategy, Long says, could be applied in careers ranging from credit card fraud detection to airline flight scheduling.

Coincidentally, Long was a contestant on another game show, The Price Is Right, where her numbers expertise won her two new cars. In a Washington Post editorial, she wrote about how she used statistics to maximize her chances of winning on the show.

Among Long’s newest interests is improving patient health literacy. For many patients faced with a disease diagnosis, the amount of available — and often conflicting — information can be overwhelming. Whereas consumers are demanding more transparency within entrenched industries like health insurance markets, Long sees disturbing evidence of the average person’s misunderstanding of his or her own illness, such as why a certain course of treatment might be recommended for specific cancers. She plans to embark on future research to better understand why some patients might overestimate their risks in the face of serious disease and how they can become more literate around their treatment options.



Ph.D. Management Science and Engineering, 2008, Stanford University

M.S. Management Science and Engineering, 2005, Stanford University

B.S. Operations Research, 2003, Cornell University



Poets & Quants Best 40 Under 40 Professors, 2017

Dean George W. Robbins Assistant Professor Teaching Award, UCLA Anderson, 2016

UCLA Faculty Career Development Award, 2015

Seth Bonder Research Fellowship Award, 2015

Stanford University Distinguished Alumni Scholar Award, 2010



Long EF, Nohdurft E, Spinler. (2017) Spatial Resource Allocation for Emerging Epidemics: A Comparison of Greedy, Myopic, and Dynamic PoliciesManufacturing & Service Operations Management, In press.

Long EF, Mathews KS. (2017) The Boarding Patient: Effects of ICU and Hospital Occupancy Surges on Patient FlowProduction and Operations Management, In press.

Nohdurft E, Long EF, Spinler S. (2017) Was Angelina Jolie Right? Optimizing Cancer Prevention Strategies among BRCA Mutation CarriersDecision Analysis, 14(3):1-31.

Long EF, Ganz PA. (2015) Cost-effectiveness of Universal BRCA1/2 ScreeningJAMA Oncology, Sep:1-2.

Mathews KS, Long EF. (2015) A Conceptual Framework for Improving Critical Care Patient Flow and Bed Use. Annals of the American Thoracic Society, 12(6):886-894.

Alistar SS, Long EF, Brandeau ML, Beck EJ. (2014) HIV Epidemic Control--A Model for Optimal Allocation of Prevention and Treatment Resources. Health Care Management Science, 17(2):162-181.

Long EF, Swain GW, Mangi AA. (2013) Comparative Survival and Cost-Effectiveness of Advanced Therapies for End-Stage Heart FailureCirculation: Heart Failure, 7(3):470-478.

Long EF, Stavert RR. (2013) Portfolios of Biomedical HIV Interventions in South Africa: A Cost-Effectiveness AnalysisJournal of General Internal Medicine, 28(10):1294-1301.

Eaton JW, Johnson LF, Salomon JA, et al. (2012) HIV Treatment as Prevention: Systematic Comparison of Mathematical Models of the Potential Impact of Antiretroviral Therapy on HIV Incidence in South Africa. PLoS Medicine, 9(7):e1001245.

Long EF. (2011) HIV Screening via Fourth-Generation Immunoassay or Nucleic Acid Amplification Test in the United States: A Cost-Effectiveness AnalysisPLoS One, 6(11):1-10.

Long EF, Owens DK. (2011) The Cost-Effectiveness of a Modestly Effective HIV Vaccine in the United States. Vaccine, 29(36):6113-6124.

Juusola JL, Brandeau ML, Long EF, Owens DK, Bendavid E. (2011) The Cost-Effectiveness of Symptom-Based Testing and Routine Screening for Acute HIV Infection in Men Who Have Sex with Men in the United States. AIDS, 25(14):1779-1787.

Long EF, Brandeau ML, Owens DK. (2010) The Cost-Effectiveness and Population Outcomes of Expanded HIV Screening and Antiretroviral Treatment in the United States. Annals of Internal Medicine, 153(12):778-789.

Long EF, Brandeau ML, Owens DK. (2009) Potential Population Health Outcomes and Expenditures of HIV Vaccination Strategies in the United States. Vaccine, 27(39):5402-5410.

Long EF, Brandeau ML. (2009) OR's Next Top Model: Decision Models for Infectious Disease Control. In Tutorials in Operations Research. Institute for Operations Research and Management Sciences (INFORMS).

Long EF, Vaidya NK, Brandeau ML. (2008) Controlling Co-epidemics: Analysis of HIV and Tuberculosis Infection Dynamics. Operations Research, 56(6):1366-1381.

Long EF, Brandeau ML, Galvin CM, Vinichenko T, Tole SP, Schwartz A, Sanders GD, Owens DK. (2006) Effectiveness and Cost-Effectiveness of Strategies to Expand Antiretroviral Therapy in St. Petersburg, Russia. AIDS, 20:2207-2215.