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Vaccination Planning under Uncertainty, with Application to Covid-19

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  • Charles F. Manski

Abstract

Vaccination against infectious disease may be beneficial to reduce illness in vaccinated persons and disease transmission across the population. The welfare-economic practice of specifying a social welfare function and considering a planner who seeks to optimize welfare provides a constructive framework to evaluate vaccination policy. This paper characterizes choice of vaccination policy as a planning problem that aims to minimize the social cost of illness and vaccination. Manski (2010, 2017) studied vaccination as a problem of planning under uncertainty, assuming that a planner can choose any vaccination rate or that the planner has only two options: mandate or decentralize vaccination. The analysis focused on uncertainty regarding the effect of vaccination on disease transmission. Here I weaken the assumptions to recognize multiple uncertainties relevant to evaluation of policy for vaccination against COVID-19. These include uncertainty not only about the effect of vaccination on disease transmission, but also about the fraction of susceptible persons in the population, the effectiveness of vaccination in reducing illness and infectiousness, and the health risks associated with vaccination. The paper considers planning under ambiguity using the minimax and minimax-regret criteria, as well as planning using a subjective probability distribution on unknown quantities. It develops algorithms that may be applied flexibly to determine policy choices with specified degrees and types of uncertainty.

Suggested Citation

  • Charles F. Manski, 2021. "Vaccination Planning under Uncertainty, with Application to Covid-19," NBER Working Papers 28446, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:28446
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    References listed on IDEAS

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    1. Charles F. Manski, 2017. "Mandating vaccination with unknown indirect effects," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 19(3), pages 603-619, June.
    2. Charles F. Manski, 2013. "Identification of treatment response with social interactions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-23, February.
    3. Hudgens, Michael G. & Halloran, M. Elizabeth, 2008. "Toward Causal Inference With Interference," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 832-842, June.
    4. Francis, Peter J., 1997. "Dynamic epidemiology and the market for vaccinations," Journal of Public Economics, Elsevier, vol. 63(3), pages 383-406, February.
    5. Manski, Charles F. & Molinari, Francesca, 2021. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, Elsevier, vol. 220(1), pages 181-192.
    6. Brito, Dagobert L. & Sheshinski, Eytan & Intriligator, Michael D., 1991. "Externalities and compulsary vaccinations," Journal of Public Economics, Elsevier, vol. 45(1), pages 69-90, June.
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Immunization

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    Cited by:

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    2. L'aszl'o Czaller & GergH{o} T'oth & Bal'azs Lengyel, 2021. "Vaccine allocation to blue-collar workers," Papers 2104.04639, arXiv.org.

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    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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