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A multi-stage stochastic programming approach to epidemic resource allocation with equity considerations

Author

Listed:
  • Xuecheng Yin

    (New Jersey Institute of Technology, Mechanical Engineering Center)

  • İ. E. Büyüktahtakın

    (New Jersey Institute of Technology, Mechanical Engineering Center)

Abstract

Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multi-stage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.

Suggested Citation

  • Xuecheng Yin & İ. E. Büyüktahtakın, 2021. "A multi-stage stochastic programming approach to epidemic resource allocation with equity considerations," Health Care Management Science, Springer, vol. 24(3), pages 597-622, September.
  • Handle: RePEc:kap:hcarem:v:24:y:2021:i:3:d:10.1007_s10729-021-09559-z
    DOI: 10.1007/s10729-021-09559-z
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    Cited by:

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    4. Bushaj, Sabah & Büyüktahtakın, İ. Esra & Haight, Robert G., 2022. "Risk-averse multi-stage stochastic optimization for surveillance and operations planning of a forest insect infestation," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1094-1110.
    5. Biswas, Debajyoti & Alfandari, Laurent, 2022. "Designing an optimal sequence of non‐pharmaceutical interventions for controlling COVID-19," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1372-1391.
    6. Yin, Xuecheng & Büyüktahtakın, İ. Esra & Patel, Bhumi P., 2023. "COVID-19: Data-Driven optimal allocation of ventilator supply under uncertainty and risk," European Journal of Operational Research, Elsevier, vol. 304(1), pages 255-275.
    7. Dianne Villicaña-Cervantes & Omar J. Ibarra-Rojas, 2024. "Accessible location of mobile labs for COVID-19 testing," Health Care Management Science, Springer, vol. 27(1), pages 1-19, March.
    8. Wang, Qingyi & Liu, Zhuomeng & Jiang, Peng & Luo, Li, 2022. "A stochastic programming model for emergency supplies pre-positioning, transshipment and procurement in a regional healthcare coalition," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).

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