Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data

  1. Brian Lee
  2. Ahmed Abdul Quadeer
  3. Muhammad Saqib Sohail
  4. Elizabeth Finney
  5. Syed Faraz Ahmed
  6. Matthew R McKay
  7. John P Barton

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Abstract

New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could improve our understanding of viral biology and highlight new variants that warrant further study. Here we develop a generic, analytical epidemiological model to infer the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that substantially affect the transmission rate, both within and outside the Spike protein. The mutations that we infer to have the largest effects on transmission are strongly supported by experimental evidence. Importantly, our model detects lineages with increased transmission even at low frequencies. As an example, we infer significant transmission advantages for the Alpha, Delta, and Omicron variants shortly after their appearances in regional data, when their local frequencies were only around 1-2%. Our model thus facilitates the rapid identification of variants and mutations that affect transmission from genomic surveillance data.

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  1. Version published to 10.1101/2021.12.31.21268591v3 on medRxiv
  2. Version published to 10.1101/2021.12.31.21268591v2 on medRxiv
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    SciScore for 10.1101/2021.12.31.21268591: (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
    Branching process simulations: We implemented the superspreading branching process for the number of infected individuals in Python.
    Python
    suggested: (IPython, RRID:SCR_001658)
    The effective reproductive number of the ath variant at time t isWe used MATLAB to simulate the SIR model under a scenario where the number of newly-infected individuals continues to increase and then remains fixed (Supplementary Fig. 2), and a scenario where we fix ra = 1 and adapt the transmission rate over time such that the system follows the typical SIR dynamics (Supplementary Fig. 3).
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    To overcome limitations of current methods, we developed a flexible, SIR-based epidemiological model that provides analytical estimates for the transmission effects of SNVs from genomic surveillance data. Applying our model to SARS-CoV-2 data, we identified SNVs that substantially increase viral transmission, including both experimentally-validated Spike mutations and other, less-studied mutations that may be promising targets for future investigation. We further explored the effects of travel and competition between variants on inferred changes in transmission, using the history of 20E (EU1) as an example. Importantly, we found that our model is sensitive enough to detect substantial transmission advantages for variants such as Alpha and Delta even when they comprised only a small fraction of the total number of infections in a region, thus providing an “early warning” for more transmissible variants. Further monitoring will be important to identify and characterize new variants as they arise. The Omicron variant that was recently detected in South Africa provides one such example. While the data in our study only extends to August 6th, 2021, we would estimate a selection coefficient of 55.2% for Omicron based on the mutations that it shares with previous variants alone. While our study has focused on SARS-CoV-2, the epidemiological model that we have developed is very general. The same methodology could be applied to study the transmission of other pathogens such as influen...

    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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  4. Version published to 10.1101/2021.12.31.21268591v1 on medRxiv