An inventory-location optimization model for equitable influenza vaccine distribution in developing countries during the COVID-19 pandemic

Rastegar, Mehdi; Tavana, Madjid; Meraj, Afshin; Mina, Hassan

doi:10.1016/j.vaccine.2020.12.022

Cited by 106 publications

(76 citation statements)

References 24 publications

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“…Sometimes it is impossible to provide vaccines for all members of society. Therefore, equitable distribution becomes a critical consideration and assumption in vaccine distribution networks (Enayati & Özaltın, 2020 ; Rastegar et al, 2021 ). Distribution of COVID-19 vaccines requires cold, very cold, and ultra-cold refrigeration.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

“…Rastegar et al ( 2021 ) proposed a mathematical model for equitable influenza vaccine distribution. The current study presents a MILP model for equitable distribution of the COVID-19 vaccines.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

“…The current study presents a MILP model for equitable distribution of the COVID-19 vaccines. The model proposed by Rastegar et al ( 2021 ) is a single-product model. However, the current study addresses the need for a multi-product model for COVID-19 vaccine distribution requiring cold, very cold, and ultra-cold refrigeration.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

“…The ordering and delivery times of vaccines can vary in the current study because of the potential waiting time between ordering and receiving the vaccines. However, the model proposed by Rastegar et al ( 2021 ) does not consider waiting time because vaccines are provided in the same period they are ordered.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

“…They divided the population into several subgroups and prevented the epidemic outbreaks by allocating the necessary vaccines to each subgroup equitably. Rastegar et al ( 2021 ) went one step further and developed a MILP model for equitable influenza vaccine distribution by considering the location-inventory problem under pandemic COVID-19 conditions. The possibility of storage for future periods, being faced with a shortage, and budget constraints are among the practical assumptions considered in this research.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Developing countries scramble to contain and mitigate the spread of coronavirus disease 2019 (COVID-19), and world leaders demand equitable distribution of vaccines to trigger economic recovery. Although numerous strategies, including education, quarantine, and immunization, have been used to control COVID-19, the best method to curb this disease is vaccination. Due to the high demand for COVID 19 vaccine, developing countries must carefully identify and prioritize vulnerable populations and rationalize the vaccine allocation process. This study presents a mixed-integer linear programming model for equitable COVID-19 vaccine distribution in developing countries. Vaccines are grouped into cold, very cold, and ultra-cold categories where specific refrigeration is required for their storage and distribution. The possibility of storage for future periods, facing a shortage, budgetary considerations, manufacturer selection, order allocation, time-dependent capacities, and grouping of the heterogeneous population are among the practical assumptions in the proposed approach. Real-world data is used to demonstrate the efficiency and effectiveness of the mathematical programming approach proposed in this study.

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Section: Motivation and Contributionsmentioning

confidence: 99%

Section: Motivation and Contributionsmentioning

confidence: 99%

Section: Motivation and Contributionsmentioning

confidence: 99%

Section: Motivation and Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

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Where to locate COVID‐19 mass vaccination facilities?

Bertsimas

Digalakis

Jacquillat

et al. 2021

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The outbreak of COVID‐19 led to a record‐breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near‐end impact of the pandemic? In the United States in particular, the new Biden administration is launching mass vaccination sites across the country, raising the obvious question of where to locate these clinics to maximize the effectiveness of the vaccination campaign. This paper tackles this question with a novel data‐driven approach to optimize COVID‐19 vaccine distribution. We first augment a state‐of‐the‐art epidemiological model, called DELPHI, to capture the effects of vaccinations and the variability in mortality rates across age groups. We then integrate this predictive model into a prescriptive model to optimize the location of vaccination sites and subsequent vaccine allocation. The model is formulated as a bilinear, nonconvex optimization model. To solve it, we propose a coordinate descent algorithm that iterates between optimizing vaccine distribution and simulating the dynamics of the pandemic. As compared to benchmarks based on demographic and epidemiological information, the proposed optimization approach increases the effectiveness of the vaccination campaign by an estimated 20%, saving an extra 4000 extra lives in the United States over a 3‐month period. The proposed solution achieves critical fairness objectives—by reducing the death toll of the pandemic in several states without hurting others—and is highly robust to uncertainties and forecast errors—by achieving similar benefits under a vast range of perturbations.

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Quantifying the benefits of customized vaccination strategies: A network‐based optimization approach

Butenko

2023

Naval Research Logistics

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We study the problem of designing vaccine distribution strategies that maximally mitigate the negative impact of an infectious disease outbreak. This is achieved through a multiperiod optimization‐based framework that embeds important subject‐specific risk and contact information into the decision‐making process. By analyzing the structure of the resulting optimization problem, we identify key structural properties which we use to construct a globally convergent solution scheme (suitable for smaller problem instances) and two, more scalable, heuristic schemes. We demonstrate the benefits of the considered framework through a case study on COVID‐19 in Texas. Our results highlight the importance of considering risk and contact information as doing so substantially reduces the total expected number of fatalities over conventional compartmental‐based approaches. These findings indicate that customization can have a significant benefit, particularly for community‐scale planning.

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An inventory-location optimization model for equitable influenza vaccine distribution in developing countries during the COVID-19 pandemic

Cited by 106 publications

References 24 publications

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

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

Where to locate COVID‐19 mass vaccination facilities?

Quantifying the benefits of customized vaccination strategies: A network‐based optimization approach

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