A decision support system for prioritised COVID-19 two-dosage vaccination allocation and distribution

Area of Science:

• Public Health
• Operations Research
• Health Informatics

Background:

• Effective COVID-19 vaccine distribution is critical for pandemic control.
• Urban settings present unique logistical challenges for mass vaccination campaigns.
• Existing distribution models may not adequately prioritize vulnerable populations.

Purpose of the Study:

• To propose a novel decision support system (DSS) for optimizing COVID-19 vaccine distribution in a large urban area.
• To develop a priority-based allocation strategy focusing on vulnerable populations.
• To assess the efficiency and resource requirements of the proposed distribution model.

Main Methods:

• Integration of Geographic Information Systems (GIS), analytics, and simulation.
• Application of hierarchical heuristic-simulation procedures for demand allocation and distribution optimization.
• Case study in Melbourne, Australia, simulating limited, excessive, and disruption vaccine supply scenarios.

Main Results:

• The DSS successfully prioritizes vaccine allocation to the most vulnerable segments of the population.
• Vaccine distribution is achievable with existing logistics, utilizing only 10 vehicles.
• Prioritized distribution plans are more effective in curbing infection spread and reducing mortality compared to non-prioritized methods.

Conclusions:

• The proposed DSS enables effective, priority-based COVID-19 vaccine distribution in urban environments.
• The system enhances pandemic response by allowing real-time decision-making and adaptation to supply chain disruptions.
• This approach optimizes resource utilization and improves public health outcomes during vaccination campaigns.