Mechanism of optimal time-course COVID-19 vaccine prioritization based on non-Markovian steady-state prediction

  1. Mi Feng
  2. Liang Tian
  3. Changsong Zhou

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Abstract

Vaccination is essential for controlling the coronavirus disease (COVID-19) pandemic. An effective time-course strategy for the allocation of COVID-19 vaccines is crucial given that the global vaccine supply will still be limited in some countries/regions in the near future and that mutant strains have emerged and will continue to spread worldwide. Both asymptomatic and symptomatic transmission have played major roles in the COVID-19 pandemic, which can only be properly described as a typical non-Markovian process. However, the prioritization of vaccines in the non-Markovian framework still lacks sufficient research, and the underlying mechanism of the time-course vaccine allocation optimization has not yet been uncovered. In this paper, based on an age-stratified compartmental model calibrated through clinical and epidemiological data, we propose optimal vaccination strategies (OVS) through steady-state prediction in the non-Markovian framework. This OVS outperforms other empirical vaccine prioritization approaches in minimizing cumulative infections, cumulative deaths, or years of life lost caused by the pandemic. We found that there exists a fast decline in the prevention efficiency of vaccination if vaccines are solely administered to a selected age group, which indicates that the widely adopted strategy to continuously vaccinate high-risk group is not optimal. Through mathematical analysis of the model, we reveal that dynamic vaccine allocations to combinations of different age groups is necessary to achieve optimal vaccine prioritization. Our work not only provides meaningful references for vaccination in countries currently lacking vaccines and for vaccine allocation strategies to prevent mutant strains in the future, but also reveals the mechanism of dynamic vaccine allocation optimization, forming a theoretical and modelling framework empirically applicable to the optimal time-course prioritization.

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    SciScore for 10.1101/2021.10.11.21264836: (What is this?)

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

    Table 1: Rigor

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    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    For example, in our model, the non-Markovian infection mostly occurs around the symptom onset according to clinical data, and can overcome the limitations of Markovian assumption characterized by constant infection rates during different symptom periods, which can underestimate the impact of transmission. In addition, optimization approaches and metrics are essential to identify the groups to be prioritized for vaccination. Matrajt et al. and Bubar et al. considered static optimization, wherein the vaccine allocation and administration strategies did not change over time [3, 4]. Similar to Buckner et al. [5], our optimization is also dynamic, adjusting for changing epidemiological transmission over a period; however, our optimization target of each vaccine allocation adjustment is to minimize the corresponding metrics in steady state, while Buckner et al. focused on optimizing the objectives in the transient state. Because vaccinating old people can reduce the cumulative deaths effectively in the initial stage of vaccination, as we demonstrated, optimizing a short-term prevention effect in reducing the deaths may entail vaccinating older essential workers, which was the conclusion made by Buckner et al. While the optimal vaccine distribution strategies differ across countries as the model is calibrated by actual situations, fortunately, we can also elucidate some common features among the vaccination strategies of the three countries, i.e., the United States, Germany and Braz...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

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

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

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  2. Version published to 10.1101/2021.10.11.21264836v1 on medRxiv