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

Abstract

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.

Multi-period vaccine allocation model in a pandemic: A case study of COVID-19 in Australia

“…These are difficult questions to answer due to the inadequate and uncertain supply of vaccines especially in the case of an unexpected outbreak and the existence of various competing priority groups. Since the supply of the vaccine is limited, it is critical for governments to prioritize and allocate a vaccine based on the vulnerability of certain populations groups ( Tavana et al, 2021 ). Brandeau et al (2003) posit that a range of factors such as the infection prevalence and incidence, population size, and characteristics of prevention programs imposed by governments affect the optimal resource allocation in a pandemic.…”

COVID-19 vaccine distribution planning using a congested queuing system—A real case from Australia

“… – ✓ – – – – ✓ – ✓ USA Mathematical model (linear programming) Sazvar et al (2021) To understand the application of capacity planning in terms of redundancy and design a supply chain network that is resilient toward the demand side. – – – ✓ – ✓ ✓ ✓ – Iran Mathematical model (robust fuzzy optimisation) Shim (2021) To determine optimal vaccine allocation for minimising infections, deaths, and years of life lost while accounting for population factors – – – – – – – – ✓ South Korea Mathematical model (age-structured dynamic model) Sinha et al (2021) To identify conditions under which a strategic inventory reserve policy cannot be practically implemented to meet service level targets ✓ ✓ ✓ – – – – ✓ ✓ India Mathematical model (game theory) Tavana et al (2021) To develop equitable COVID-19 vaccine distribution in developing countries. ✓ ✓ – – …”

“…For example, while both Pfizer–Biontech (known as Pfizer) and AstraZeneca–Oxford (known as AstraZeneca) are COVID-19 vaccines with two doses that must be given in different time windows, Johnson & Johnson’s Janssen (known as J&J) vaccine is comprised of only one dose. The differences in transportation and storage, efficiency, and costs also vary depending on which vaccine has priority ( Abbasi et al, 2020 , Tavana et al, 2021 ). Therefore, it is imperative to develop distribution mechanisms to respond effectively and efficiently to such challenges and mitigate likely disparities.…”

A mathematical model for managing the multi-dimensional impacts of the COVID-19 pandemic in supply chain of a high-demand item

“…Similarly, Nagurney ( 2021 ) used the same approach to develop a framework to capture the labour constraints in fresh produce products during this pandemic, and Ivanov and Dolgui ( 2020b ) introduced the concept of intertwined supply network (ISN), suggesting that major SC disruptions like the COVID-19 pandemic require resistance at the scale of viability. A mixed-integer linear modelling approach was used to analyze the impact of this pandemic on a drug supply network (Lozano-Diez et al, 2020 ), to provide an equitable vaccine distribution framework in developing countries (Tavana et al, 2021 ), and to reconfigure food grain SC networks by considering government guidelines (D. Sharma et al, 2021 ). A multi-objective mixed-integer linear programming modelling was also used to develop a framework for sustainable, responsive, and resilient mixed SC networks (Vali-Siar & Roghanian, 2022 ).…”