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COVID-19 vaccination policies under uncertain transmission characteristics using stochastic programming

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  • Krishna Reddy Gujjula
  • Jiangyue Gong
  • Brittany Segundo
  • Lewis Ntaimo

Abstract

We develop a new stochastic programming methodology for determining optimal vaccination policies for a multi-community heterogeneous population. An optimal policy provides the minimum number of vaccinations required to drive post-vaccination reproduction number to below one at a desired reliability level. To generate a vaccination policy, the new method considers the uncertainty in COVID-19 related parameters such as efficacy of vaccines, age-related variation in susceptibility and infectivity to SARS-CoV-2, distribution of household composition in a community, and variation in human interactions. We report on a computational study of the new methodology on a set of neighboring U.S. counties to generate vaccination policies based on vaccine availability. The results show that to control outbreaks at least a certain percentage of the population should be vaccinated in each community based on pre-determined reliability levels. The study also reveals the vaccine sharing capability of the proposed approach among counties under limited vaccine availability. This work contributes a decision-making tool to aid public health agencies worldwide in the allocation of limited vaccines under uncertainty towards controlling epidemics through vaccinations.

Suggested Citation

  • Krishna Reddy Gujjula & Jiangyue Gong & Brittany Segundo & Lewis Ntaimo, 2022. "COVID-19 vaccination policies under uncertain transmission characteristics using stochastic programming," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-21, July.
    • Handle: RePEc:plo:pone00:0270524
    DOI: 10.1371/journal.pone.0270524
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