Pricing the COVID-19 vaccine: A mathematical approach

Martonosi, Susan E.; Behzad, Banafsheh; Cummings, Kayla

doi:10.1016/j.omega.2021.102451

Cited by 30 publications

(17 citation statements)

References 12 publications

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“…using age-stratified population subgroups. The modeling of the impact of vaccine allocation decisions could be refined by incorporating additional features such as competition effects in the vaccine market (Martonosi et al, 2021). The analysis of the impact of coalition among agents could also be investigated to develop further incentive mechanisms to improve global vaccination efforts in the context of a pandemic.…”

Section: Discussionmentioning

confidence: 99%

Vaccine allocation policy optimization and budget sharing mechanism using Thompson sampling

Rey¹,

Hammad²,

Saberi³

2021

Preprint

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The optimal allocation of vaccines to population subgroups over time is a challenging health care management problem. In the context of a pandemic, the interaction between vaccination policies adopted by multiple agents and the cooperation (or lack thereof) creates a complex environment that affects the global transmission dynamics of the disease. In this study, we take the perspective of decision-making agents that aim to minimize the size of their susceptible populations and must allocate vaccine under limited supply. We assume that vaccine efficiency rates are unknown to agents and we propose an optimization policy based on Thompson sampling to learn mean vaccine efficiency rates over time. Furthermore, we develop a budget-balanced resource sharing mechanism to promote cooperation among agents. We apply the proposed framework to the COVID-19 pandemic. We use a raster model of the world where agents represent the main countries worldwide and interact in a global mobility network to generate multiple problem instances. Our numerical results show that the proposed vaccine allocation policy achieves a larger reduction in the number of susceptible individuals, infections and deaths globally compared to a population-based policy. In addition, we show that, under a fixed global vaccine allocation budget, most countries can reduce their national number of infections and deaths by sharing their budget with countries with which they have a relatively high mobility exchange. The proposed framework can be used to improve policy-making in health care management by national and global health authorities.

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Section: Discussionmentioning

confidence: 99%

Vaccine allocation policy optimization and budget sharing mechanism using Thompson sampling

Rey¹,

Hammad²,

Saberi³

2021

Preprint

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“…[50] analyze the impact of various COVID-19 vaccine strategies on COVID-19 case numbers and mortality under a limited supply scenario, as well as varied vaccine efficacy and speed of vaccination for mass vaccination. [51] address the COVID-19 pricing issue and use optimization and game theoretic approaches to model the vaccine market as a duopoly with two manufacturers, Pfizer-BioNTech and Moderna.…”

Section: Literature Reviewmentioning

confidence: 99%

Bi-objective optimization for a multi-period COVID-19 vaccination planning problem

Tang

Bai

et al. 2022

Omega

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“…Other studies have referred to but not been limited to: pricing the COVID-19 vaccine [38]; myths and conspiracy theories on vaccines and COVID-19 [39]; the willingness to receive a COVID-19 vaccine with and without the emergency use authorization [40]; vaccine preferences based on efficacy [41]; comparisons among different individual groups regarding vaccine acceptance [42]; the opinions of medical students, nurses and physicians related to COVID-19 vaccination in Colombia [43], Germany [44], Hong Kong [45], Romania [46] and the US [47][48][49]; COVID-19 dynamics after an immunization program in Israel [50]; the protection offered by the Pfizer-BioNTech COVID-19 booster in Israel [7]; the need to address the hesitancy [51]; and a review of vaccine hesitancy during COVID-19 [52].…”

Section: Literature Reviewmentioning

confidence: 99%

New Wave of COVID-19 Vaccine Opinions in the Month the 3rd Booster Dose Arrived

et al. 2022

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Vaccination has been proposed as one of the most effective methods to combat the COVID-19 pandemic. Since the day the first vaccine, with an efficiency of more than 90%, was announced, the entire vaccination process and its possible consequences in large populations have generated a series of discussions on social media. Whereas the opinions triggered by the administration of the initial COVID-19 vaccine doses have been discussed in depth in the scientific literature, the approval of the so-called 3rd booster dose has only been analyzed in country-specific studies, primarily using questionnaires. In this context, the present paper conducts a stance analysis using a transformer-based deep learning model on a dataset containing 3,841,594 tweets in English collected between 12 July 2021 and 11 August 2021 (the month in which the 3rd dose arrived) and compares the opinions (in favor, neutral and against) with the ones extracted at the beginning of the vaccination process. In terms of COVID-19 vaccination hesitance, an analysis based on hashtags, n-grams and latent Dirichlet allocation is performed that highlights the main reasons behind the reluctance to vaccinate. The proposed approach can be useful in the context of the campaigns related to COVID-19 vaccination as it provides insights related to the public opinion and can be useful in creating communication messages to support the vaccination campaign.

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Pricing the COVID-19 vaccine: A mathematical approach

Cited by 30 publications

References 12 publications

Vaccine allocation policy optimization and budget sharing mechanism using Thompson sampling

Vaccine allocation policy optimization and budget sharing mechanism using Thompson sampling

Bi-objective optimization for a multi-period COVID-19 vaccination planning problem

New Wave of COVID-19 Vaccine Opinions in the Month the 3rd Booster Dose Arrived

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