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Pricing the COVID-19 vaccine: A mathematical approach

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

Abstract

According to the World Health Organization, development of the COVID-19 vaccine is occurring in record time. Administration of the vaccine has started the same year as the declaration of the COVID-19 pandemic. The United Nations emphasized the importance of providing COVID-19 vaccines as a global public good, which is accessible and affordable world-wide. Pricing the COVID-19 vaccines is a controversial topic. We use optimization and game theoretic approaches to model the COVID-19 U.S. vaccine market as a duopoly with two manufacturers Pfizer-BioNTech and Moderna. The results suggest that even in the context of very high production and distribution costs, the government can negotiate prices with the manufacturers to keep public sector prices as low as possible while meeting demand and ensuring each manufacturer earns a target profit. Furthermore, these prices are consistent with those currently predicted in the media.

Suggested Citation

  • Martonosi, Susan E. & Behzad, Banafsheh & Cummings, Kayla, 2021. "Pricing the COVID-19 vaccine: A mathematical approach," Omega, Elsevier, vol. 103(C).
    • Handle: RePEc:eee:jomega:v:103:y:2021:i:c:s0305048321000608
    DOI: 10.1016/j.omega.2021.102451
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    1. Elizabeth F. R. Gingerich, 2020. "Values-Based Leadership: Exploring Exemplary Approaches," Management for Professionals, in: Satinder Dhiman & Joan Marques (ed.), New Horizons in Positive Leadership and Change, chapter 9, pages 159-173, Springer.
    2. Manuel Funke & Moritz Schularick & Christoph Trebesch, 2023. "Populist Leaders and the Economy," American Economic Review, American Economic Association, vol. 113(12), pages 3249-3288, December.
    3. Ahearn, Bertie & Singh Ahluwalia, Montek & Ahmed, Masood & Alphandéry, Edmond & Altwaijri, HE Dr Abdulaziz Altwaijri & Amato, Giuliano & Amersi, Mohamed & Arbour, Louise & Aria, Óscar & Aziz, Shaukat , 2020. "The COVID-19 pandemic: a letter to G20 leaders," LSE Research Online Documents on Economics 104370, London School of Economics and Political Science, LSE Library.
    4. Sanjay Mehrotra & Hamed Rahimian & Masoud Barah & Fengqiao Luo & Karolina Schantz, 2020. "A model of supply‐chain decisions for resource sharing with an application to ventilator allocation to combat COVID‐19," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(5), pages 303-320, August.
    5. Ivanov, Dmitry, 2020. "Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
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    1. Tang, Lianhua & Li, Yantong & Bai, Danyu & Liu, Tao & Coelho, Leandro C., 2022. "Bi-objective optimization for a multi-period COVID-19 vaccination planning problem," Omega, Elsevier, vol. 110(C).
    2. Kamran, Mehdi A. & Kia, Reza & Goodarzian, Fariba & Ghasemi, Peiman, 2023. "A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    3. Ekinci, Esra & Mangla, Sachin Kumar & Kazancoglu, Yigit & Sarma, P.R.S. & Sezer, Muruvvet Deniz & Ozbiltekin-Pala, Melisa, 2022. "Resilience and complexity measurement for energy efficient global supply chains in disruptive events," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    4. Batabyal, Amitrajeet & Beladi, Hamid, 2022. "City and Regional Demand for Vaccines Whose Supply Arises from Competition in a Bertrand Duopoly," MPRA Paper 113758, University Library of Munich, Germany, revised 28 Jun 2022.
    5. Emanuele Blasioli & Bahareh Mansouri & Srinivas Subramanya Tamvada & Elkafi Hassini, 2023. "Vaccine Allocation and Distribution: A Review with a Focus on Quantitative Methodologies and Application to Equity, Hesitancy, and COVID-19 Pandemic," SN Operations Research Forum, Springer, vol. 4(2), pages 1-32, June.
    6. Rey, David & Hammad, Ahmed W. & Saberi, Meead, 2023. "Vaccine allocation policy optimization and budget sharing mechanism using reinforcement learning," Omega, Elsevier, vol. 115(C).
    7. Wang, Fan & Xu, Danni & Zhuo, Xiaopo & Zhang, Chao & Liu, Yaoqi, 2022. "Improving consumer welfare in vaccine market: Pricing, government subsidies and consumer awareness," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    8. Choi, Tsan-Ming & Shi, Xiutian, 2022. "Reducing supply risks by supply guarantee deposit payments in the fashion industry in the “new normal after COVID-19”," Omega, Elsevier, vol. 109(C).
    9. Shaker Ardakani, Elham & Gilani Larimi, Niloofar & Oveysi Nejad, Maryam & Madani Hosseini, Mahsa & Zargoush, Manaf, 2023. "A resilient, robust transformation of healthcare systems to cope with COVID-19 through alternative resources," Omega, Elsevier, vol. 114(C).
    10. Gilani, Hani & Sahebi, Hadi, 2022. "A data-driven robust optimization model by cutting hyperplanes on vaccine access uncertainty in COVID-19 vaccine supply chain," Omega, Elsevier, vol. 110(C).

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