An optimization framework for COVID-19 vaccine allocation and inventory management: A case study.

Area of Science:

• Operations Research
• Public Health Policy
• Epidemiology

Background:

• Global COVID-19 vaccination programs face challenges including hesitancy, unequal distribution, new variants, and potential need for cyclical vaccination.
• Effective mass vaccination requires robust allocation and inventory management strategies to overcome these hurdles.

Purpose of the Study:

• To develop a bi-objective integrated framework for vaccine allocation and inventory management.
• To minimize system costs while maximizing vaccination service levels.
• To create a model adaptable to current vaccination policies with minimal strategic changes.

Main Methods:

• A dynamic planning approach was used to integrate factors like multiple vaccine types, perishability, demand uncertainty, and motivational strategies.
• A bi-objective optimization model was formulated to balance cost minimization and service level maximization.
• The Lagrangian relaxation method was applied to enhance the model's scalability for large-sized problems.

Main Results:

• The proposed framework demonstrated significant improvements in total cost, reduction in vaccine shortages, and minimized wastage compared to the existing policy.
• The case study in Mashhad, Iran, validated the model's effectiveness and practical applicability.
• The model's dynamic planning and integration of key factors led to superior outcomes.

Conclusions:

• The developed integrated framework offers a viable solution for optimizing mass COVID-19 vaccination programs.
• The model provides a strategic advantage in managing vaccine resources efficiently, addressing key pandemic response challenges.
• Further application of this model can enhance public health preparedness and response to future vaccination campaigns.