Evaluation of the United States COVID-19 vaccine allocation strategy

  1. Md Rafiul Islam
  2. Tamer Oraby
  3. Audrey McCombs
  4. Mohammad Mihrab Chowdhury
  5. Mohammad Al-Mamun
  6. Michael G. Tyshenko
  7. Claus Kadelka

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Abstract

Anticipating an initial shortage of vaccines for COVID-19, the Centers for Disease Control (CDC) in the United States developed priority vaccine allocations for specific demographic groups in the population. This study evaluates the performance of the CDC vaccine allocation strategy with respect to multiple potentially competing vaccination goals (minimizing mortality, cases, infections, and years of life lost (YLL)), under the same framework as the CDC allocation: four priority vaccination groups and population demographics stratified by age, comorbidities, occupation and living condition (congested or non-congested).

Methods and findings

We developed a compartmental disease model that incorporates key elements of the current pandemic including age-varying susceptibility to infection, age-varying clinical fraction, an active case-count dependent social distancing level, and time-varying infectivity (accounting for the emergence of more infectious virus strains). The CDC allocation strategy is compared to all other possibly optimal allocations that stagger vaccine roll-out in up to four phases (17.5 million strategies). The CDC allocation strategy performed well in all vaccination goals but never optimally. Under the developed model, the CDC allocation deviated from the optimal allocations by small amounts, with 0.19% more deaths, 4.0% more cases, 4.07% more infections, and 0.97% higher YLL, than the respective optimal strategies. The CDC decision to not prioritize the vaccination of individuals under the age of 16 was optimal, as was the prioritization of health-care workers and other essential workers over non-essential workers. Finally, a higher prioritization of individuals with comorbidities in all age groups improved outcomes compared to the CDC allocation.

Conclusion

The developed approach can be used to inform the design of future vaccine allocation strategies in the United States, or adapted for use by other countries seeking to optimize the effectiveness of their vaccine allocation strategies.

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  1. Version published to 10.1371/journal.pone.0259700
  2. ScreenIT

    SciScore for 10.1101/2021.07.01.21259870: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data and all relevant code is available at GitHub: https://github.com/ckadelka/COVID19-CDC-allocation-evaluation and will be stored in the Zenodo repository https://doi.org/XXX upon acceptance.
    Zenodo
    suggested: (ZENODO, RRID:SCR_004129)
    Model implementation and outcomes: The model is implemented in Python 3.8 using the open source JIT compiler numba for improved run time.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    One limitation of this study is the simplifying assumption that all sub-populations exhibit the same level of vaccine hesitancy, and that hesitancy does not change over time. Incorporating these additional dynamic elements into the model would improve the accuracy of the results, but would significantly increase model complexity. Further limitations stem from uncertainties regarding key model parameters. Rates of contact between individuals of different age groups were based on extensive pre-pandemic survey work in eight European countries and inferred for the United States [18, 19]. In the absence of data, we assumed that contacts within a group in a congested living situation occur at double the rate of the same age group not living in congested conditions, and the additional contacts are with other individuals in the same congested living situation. Model results depend strongly on the contact matrix, and better information about contact rates, especially during the pandemic, could improve the accuracy of model predictions. The contagiousness of asymptomatic as well as vaccinated individuals is still not well understood. The model results were however robust to uncertainty in these parameters (table S4): variation in both phase assignments and the overall shape of the Pareto frontier were small. We did not consider reinfections in our model. Repeated infections have been reported in the literature [29], but they seem rare and a recent study suggests prolonged immunity in m...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.07.01.21259870v1 on medRxiv