A mathematical programming approach for equitable COVID-19 vaccine distribution in developing countries.

Area of Science:

• Public Health
• Operations Research
• Epidemiology

Background:

• Developing countries face challenges in controlling COVID-19 spread and achieving economic recovery.
• Equitable vaccine distribution is critical for pandemic mitigation and economic rebound.
• Vaccination is the most effective strategy against COVID-19, necessitating careful planning in resource-limited settings.

Purpose of the Study:

• To develop a mathematical model for equitable COVID-19 vaccine distribution in developing countries.
• To address practical considerations in vaccine allocation, including storage, shortages, and population heterogeneity.
• To optimize vaccine deployment for vulnerable groups and ensure efficient resource utilization.

Main Methods:

• A mixed-integer linear programming model was formulated.
• The model incorporates vaccine categories (cold, very cold, ultra-cold) and their specific storage requirements.
• Assumptions include future storage, shortage management, budget constraints, manufacturer selection, order allocation, time-dependent capacities, and population grouping.

Main Results:

• The study demonstrates the efficiency and effectiveness of the proposed mathematical programming approach.
• The model provides a framework for rationalizing vaccine allocation processes in developing countries.
• Real-world data validated the practical applicability of the developed model.

Conclusions:

• The mathematical model offers a robust solution for equitable COVID-19 vaccine distribution in developing nations.
• Prioritizing vulnerable populations and optimizing allocation are key to successful vaccination campaigns.
• This approach supports both public health goals and economic recovery efforts.