Fair and Effective Vaccine Allocation During a Pandemic

Güneş Erdoğan, Sibel Salman, Eda Yucel, Parinaz Kiavash

Research output: Contribution to journalArticlepeer-review


This paper presents a novel model for the Vaccine Allocation Problem (VAP), which aims to allocate the available vaccines to population locations over multiple periods during a pandemic. We model the disease progression and the impact of vaccination on the spread of the disease and mortality to minimise total expected mortality and location inequity in terms of mortality ratios under total vaccine supply and hospital and vaccination centre capacity limitations at the locations. The spread of the disease is modelled through an extension of the well-established Susceptible–Infected–Recovered (SIR) epidemiological model that accounts for multiple vaccine doses. The VAP is modelled as a nonlinear mixed-integer programming model and solved to optimality using the Gurobi solver. A set of scenarios with parameters regarding the COVID-19 pandemic in the UK over 12 weeks are constructed using a hypercube experimental design on varying disease spread, vaccine availability, hospital capacity, and vaccination capacity factors. The results indicate the statistical significance of vaccine availability and the parameters regarding the spread of the disease.

Original languageEnglish
Article number101895
JournalSocio-Economic Planning Sciences
Early online date18 Apr 2024
Publication statusE-pub ahead of print - 18 Apr 2024

Data Availability Statement

Data will be made available on request.


  • COVID-19 pandemic
  • Fairness
  • Nonlinear mixed integer program
  • Optimisation
  • Vaccine allocation

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty
  • Strategy and Management
  • Management Science and Operations Research

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