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Equity-based vaccine delivery by drones: Optimizing distribution in disease-prone regions

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  • Sayarshad, Hamid R.

Abstract

This study introduces a model that combines dynamic disease modeling and an optimization approach for drone-based vaccine delivery to achieve fair distribution and enhance equity in vaccine access across different regions, including rural areas and small cities. Our approach aims to achieve optimal allocation of vaccines by considering regional infection rates and equilibrium vaccination rates, which allows us to forecast vaccine demand effectively. To achieve this, we employ a region-specific dynamic disease model that considers population size, infection rates, and vaccination rates. Utilizing this dynamic disease model with a well-structured delivery network minimizes travel and healthcare costs resulting from insufficient vaccination delivery while ensuring equitable distribution. Our model also considers logistical factors specific to drone vaccine delivery, including routing and recharging plans, payload capacity, flight range, and regional vaccine demand. These considerations are crucial to addressing the unique challenges rural areas and small cities face in accessing healthcare services. This study also investigates the essential trade-offs between minimizing delivery costs and mitigating healthcare burdens during a pandemic response. We study drone vaccine delivery during the COVID-19 pandemic to validate our model, explicitly focusing on Orange County (OC) and small cities. The results of this study have important practical implications for designing drone-based vaccine delivery systems that prioritize fairness and equitable access, especially in smaller cities and rural areas. It highlights that cities with lower populations but higher transmission rates may require more vaccines, while larger cities with lower rates need fewer.

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  • Sayarshad, Hamid R., 2025. "Equity-based vaccine delivery by drones: Optimizing distribution in disease-prone regions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:transe:v:195:y:2025:i:c:s1366554525000201
    DOI: 10.1016/j.tre.2025.103979
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