Operational issues and network effects in vaccine markets

Designing an optimization model for the vaccine supply chain during the COVID-19 pandemic

The COVID-19 pandemic has affected people's lives worldwide. Among various strategies being applied to addressing such a global crisis, public vaccination has been arguably the most appropriate approach to control a pandemic. However, vaccine supply chain and management have become a new challenge for governments. In this study, a solution for the vaccine supply chain is presented to address the hurdles in the public vaccination program according to the concerns of the government and the organizations involved. For this purpose, a robust bi-level optimization model is proposed. At the upper level, the risk of mortality due to the untimely supply of the vaccine and the risk of inequality in the distribution of the vaccine is considered. All costs related to the vaccine supply chain are considered at the lower level, including the vaccine supply, allocation of candidate centers for vaccine injection, cost of maintenance and injection, transportation cost, and penalty cost due to the vaccine shortage. In addition, the uncertainty of demand for vaccines is considered with multiple scenarios of different demand levels. Numerical experiments are conducted based on the vaccine supply chain in Kermanshah, Iran, and the results show that the proposed model significantly reduces the risk of mortality and inequality in the distribution of vaccines as well as the total cost, which leads to managerial insights for better coordination of the vaccination network during the COVID-19 pandemic.

Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning

Vaccination offers health, economic, and social benefits. However, three major issues-vaccine quality, demand forecasting, and trust among stakeholders-persist in the vaccine supply chain (VSC), leading to inefficiencies. The COVID-19 pandemic has exacerbated weaknesses in the VSC, while presenting opportunities to apply digital technologies to manage it. For the first time, this study establishes an intelligent VSC management system that provides decision support for VSC management during the COVID-19 pandemic. The system combines blockchain, internet of things (IoT), and machine learning that effectively address the three issues in the VSC. The transparency of blockchain ensures trust among stakeholders. The real-time monitoring of vaccine status by the IoT ensures vaccine quality. Machine learning predicts vaccine demand and conducts sentiment analysis on vaccine reviews to help companies improve vaccine quality. The present study also reveals the implications for the management of supply chains, businesses, and government.

Optimal vaccine subsidies for endemic diseases

In Goodkin-Gold et al. (2021), we analyzed optimal subsidies for a vaccine against an epidemic outbreak like Covid-19. This companion paper alters the underlying epidemiological model to suit endemic diseases requiring continuous vaccination of new cohorts-also suiting an epidemic like Covid-19 if, following Gans (2020), one assumes peaks are leveled by social distancing. We obtain qualitatively similar results: across market structures ranging from perfect competition to monopoly, the subsidy needed to induce first-best vaccination coverage on the private market is highest for moderately infectious diseases, which invite the most free riding; extremely infectious diseases drive more consumers to become vaccinated, attenuating externalities. Stylized calibrations to HIV, among other diseases, suggest that first-best subsidies can be exorbitantly high when suppliers have market power, rationalizing alternative policies observed in practice such as bulk purchases negotiated by the government on behalf of the consumers.

Information sharing and coordination in a vaccine supply chain

Vaccination is a well-known method to protect the public against an epidemic outbreak, e.g., COVID-19. To this end, the government of a country or region would strive to achieve its target of vaccination coverage. Limited by the total vaccine capacity of public hospitals, the government may need to cooperate with private hospitals or clinics for more vaccination. Exploring in this paper government coordination of public and private resources for vaccination, we model a vaccine system consisting of a public hospital, a profit-maximizing private clinic, and self-interested individuals, under three scenarios: (1) without information sharing (concerning vaccine inventory and vaccine price), (2) with information sharing and subsidy, and (3) with information sharing and allocation. We find that, under scenario (1), the vaccine demand is fully satisfied by the public hospital and the private clinic cannot make any profit. Under scenario (2), the private clinic is willing to enter the vaccine market with a positive profit-maximizing vaccination coverage. Under scenario (3), the socially optimal vaccination coverage may be lower than that under scenario (1). Moreover, we conduct a sensitivity analysis to generate practical implications of the research findings for vaccination policy-making. Our results provide both theoretical and managerial insights on vaccine supply decision, government intervention, and vaccination coverage.

A fuzzy multi-criteria framework to identify barriers and enablers of the next-generation vaccine supply chain

Purpose

Due to the introduction of new vaccines in the child immunization program and inefficient vaccine supply chain (VSC), the universal immunization program (UIP), India is struggling to provide a full schedule of vaccination to the targeted children. In this paper, the authors investigate the critical factors for improving the performance of the existing VSC system by implementing the next-generation vaccine supply chain (NGVSC) in India.

Design/methodology/approach

The authors design a fuzzy multi-criteria framework using a fuzzy analytical hierarchical process (FAHP) and fuzzy multi-objective optimization on the basis of ratio analysis (FMOORA) to identify and analyze the critical barriers and enablers for the implementation of NGVSC. Further, the authors carry out a numerical simulation to validate the model.

Findings

The outcome of the analysis contends that demand forecasting is the topmost supply chain barrier and sustainable financing is the most important/critical enabler to facilitate the implementation of the NGVSC. In addition, the simulation reveals that the results of the study are reliable.

Social implications

The findings of the study can be useful for the child immunization policymakers of India and other developing countries to design appropriate strategies for improving existing VSC performance by implementing the NGVSC.

Originality/value

To the best of the authors’ knowledge, the study is the first empirical study to propose the improvement of VSC performance by designing the NGVSC.

Implications of government subsidy on the vaccine product R&D when the buyer is risk averse

This paper analyses the choice of subsidy offered to a vaccine supply chain with a risk-averse buyer. We find that for a higher innovation effort and level of social benefits, the per-unit production subsidy is better when there is a low innovation cost coefficient, a low level of risk aversion, or a high potential demand. Otherwise, under the opposite conditions, the R&D innovation effort subsidy should be selected. Furthermore, from an evolutionary game theoretical perspective, we also present the stability performance for the subsidies, and the results show that when the manufacturer's innovation cost coefficient is relatively low, the more profitable per-unit production subsidy may be abandoned due to its performance instability.

Designing intervention scheme for vaccine market: a bilevel programming approach

Public-interest goods benefit consumers and also generate external benefits boosting societal welfare. Despite this characteristic of these goods, their level of consumption or production are generally well below the socially desirable levels without intervention. Motivated by influenza vaccine market, this paper examines the intervention design problem for a public-interest good facing yield uncertainty in production as well as inefficiencies in distribution and allocation. The proposed mechanism considers two intervention tools with the aim of resolving the inefficiencies in the system and allowing the actors to take socially desirable decisions. The first tool is to intervene so that demand level for the good is increased; we call it demand increasing strategy. The second tool aims to support the production, allocation, and distribution by investing in research and development and better planning and enhances the availability; we call this as availability increasing strategy. The intervention design problem is based on stylized demand and availability models that take into account investments made to improve them. The model suggested is experimented by a numerical study to analyze the impact of applying proposed joint mechanism in US influenza vaccine market. The results show that proposed strategy is very effectual in terms of vaccination percentages achieved and budget savings realized beyond the current practices, and the improvement in vaccination percentages is even greater when uncertainty in the system is higher. Besides, the results suggest that as long as the parameter calibration and decision problems are solved consistently, availability can be approximated by its average value when necessary.

Intervention mechanism of healthcare service goods based on social welfare maximization in China

In this paper, we aim to establish a mathematical model to design a maximizing social welfare intervention mechanism of healthcare service goods in China. The intervention mechanism is helpful to facilitate the adoption of the healthcare service goods. We consider a research problem that regulates the supply chain system for healthcare service goods by an intervention mechanism, and two intervention strategies composed of demand-growth strategy and subsidy strategy are used to the combination of intervention mechanism. Then this paper presents a new method based on fuzzy set and bilevel programming to design the intervention mechanism. To demonstrate the effectiveness of the proposed model, we conduct a case study for Wudang personalized health package and verify our model by the specific result analysis, the result indicates that our joint intervention mechanism is helpful to achieve the target and increase social welfare.

Dose-Optimal Vaccine Allocation over Multiple Populations

Vaccination is an effective way to prevent an epidemic. It results in immunity for the vaccinated individuals, but it also reduces the infection pressure for unvaccinated people. Thus people may actually escape infection without being vaccinated: the so-called "herd effect." We analytically study the relation between the herd effect and the vaccination fraction for the seminal SIR compartmental model, which consists of a set of differential equations describing the time course of an epidemic. We prove that the herd effect is in general convex-concave in the vaccination fraction and give precise conditions on the epidemic for the convex part to arise. We derive the significant consequences of these structural insights for allocating a limited vaccine stockpile to multiple non-interacting populations. We identify for each population a unique vaccination fraction that is most efficient per dose of vaccine: our dose-optimal coverage. We characterize the solution of the vaccine allocation problem and we show the crucial importance of the dose-optimal coverage. A single dose of vaccine may be a drop in the ocean, but multiple doses together can save a population. To benefit from this, policy makers should select a subset of populations to which the vaccines are allocated. Focusing on a limited number of populations can make a significant difference, whereas allocating equally to all populations would be substantially less effective.