Modelling the interplay of SARS-CoV-2 variants in the United Kingdom

  1. N. L. Barreiro
  2. T. Govezensky
  3. C. I. Ventura
  4. M. Núñez
  5. P. G. Bolcatto
  6. R. A. Barrio

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Many COVID-19 vaccines are proving to be highly effective to prevent severe disease and to diminish infections. Their uneven geographical distribution favors the appearance of new variants of concern, as the highly transmissible Delta variant, affecting particularly non-vaccinated people. It is important to device reliable models to analyze the spread of the different variants. A key factor is to consider the effects of vaccination as well as other measures used to contain the pandemic like social behaviour. The stochastic geographical model presented here, fulfills these requirements. It is based on an extended compartmental model that includes various strains and vaccination strategies, allowing to study the emergence and dynamics of the new COVID-19 variants. The model conveniently separates the parameters related to the disease from the ones related to social behavior and mobility restrictions. We applied the model to the United Kingdom by using available data to fit the recurrence of the currently prevalent variants. Our computer simulations allow to describe the appearance of periodic waves and the features that determine the prevalence of certain variants. They also provide useful predictions to help planning future vaccination boosters. We stress that the model could be applied to any other country of interest.

Article activity feed

  1. Version published to 10.1038/s41598-022-16147-w
  2. ScreenIT

    SciScore for 10.1101/2021.11.26.21266485: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    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.
    • 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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.11.26.21266485v1 on medRxiv