COVID-19: Data-Driven optimal allocation of ventilator supply under uncertainty and risk

Xuecheng Yin, Esra Büyüktahtakın, Bhumi P. Patel

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.

Original languageEnglish (US)
Pages (from-to)255-275
Number of pages21
JournalEuropean Journal of Operational Research
Issue number1
StatePublished - Jan 1 2023

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research


  • COVID-19
  • Mean-CVaR multi-stage stochastic mixed-integer programming model
  • OR in health services
  • Pandemic resource and ventilator allocation
  • Risk-averse optimization


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