Modeling of COVID-19 Transmission Dynamics on US Population: Inter-transfer Infection in Age Groups, Mutant Variants and Vaccination Strategies

  1. Jyotirmoy Roy
  2. Samuel Heath
  3. Doraiswami Ramkrishna
  4. Shiyan Wang

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Abstract

The in-depth understanding of the dynamics of COVID-19 transmission among different age groups is of great interest for governments and health authorities so that strategies can be devised to reduce the pandemic’s detrimental effects. We developed the SIRDV-Virulence epidemiological model based on a population balance equation to study the effect of mutants of the virus and the effect of vaccination strategies on mitigating the transmission among the population in the United States. Based on the available data from the Centers for Disease Control and Prevention (CDC), we obtain the key parameters governing the dynamic evolution of the spread of the COVID-19 pandemic. In the context studied, the results show that a large fraction of infected cases comes from the adult and children populations in the presence of a mutant variant of COVID-19 with high infection rates. We further investigate the optimum vaccine distribution strategy among different age groups. Given the current situation in the United States, the results show that prioritizing children and adult vaccinations over that of seniors can contain the spread of the active cases, thereby preventing the healthcare system from being overwhelmed and minimizing subsequent deaths. The model suggests that the only option to curb the effects of this pandemic is to reduce the population of unvaccinated individuals. A higher fraction of ‘Anti/Non-vaxxers’ can lead to the resurgence of the pandemic.

Author summary

The changing dynamics of the COVID-19 pandemic are primarily due to the mutations of the SARS-CoV-2 virus. It is often seen that these mutants not only have a higher infection rate but also evade the presently administered vaccines. To consider the fact that different age population groups are affected to varied extent by these mutants, we build a mathematical model to account for the inter-transfer infection among age groups, which can predict the overall COVID-19 transmission in the United States. The parameter quantification of our mathematical model is based on the public data for infected cases, deaths and vaccinated from the Centers for Disease Control and Prevention (CDC). Additionally, our study shows that the vaccine distribution strategies should be developed with a priority given to the most infected age groups in order to curb the total infected and death cases. We also show how the ‘Anti/Non-vaxxers’ can be a potential reason for resurgence of the pandemic. These results are of immediate practical application in determining future vaccine distribution regarding to the pandemic and ensuring the health care system is ready to deal with the worst-case scenario with a very high infection rate.

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  1. ScreenIT

    SciScore for 10.1101/2021.09.25.21264118: (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

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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

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  2. Version published to 10.1101/2021.09.25.21264118v1 on medRxiv