Deviations in Predicted COVID-19 cases in the US during early months of 2021 relate to rise in B.1.526 and its family of variants

  1. P. Nandakishore
  2. M. Liu
  3. Prakash R.
  4. S. Gourneni
  5. R. Sukumaran
  6. J.M. Berman
  7. S. Iyer
  8. C. Glorioso

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Abstract

Objective

To investigate the abrogation of COVID-19 case declines from predicted rates in the US in relationship to viral variants and mutations.

Design

Epidemiological prediction and time series study of COVID-19 in the US by State.

Setting

Community testing and sequencing of COVID-19 in the US.

Participants

Time series US COVID-19 case data from the Johns Hopkins University CSSE database. Time series US Variant and Mutation data from the GISAID database.

Main outcome measures

Primary outcomes were statistical modeling of US state deviations from epidemiological predictions, percentage of COVID-19 variants, percentage of COVID-19 mutations, and reported SARS-CoV-2 infections.

Results

Deviations in epidemiological predictions of COVID-19 case declines in the North Eastern US in March 2021 were highly positively related to percentage of B.1.526 (Iota) lineage ( p < 10 e − 7) and B.1.526.2 ( p < 10 − 8) and the T95I mutation ( p < 10 e − 9). They were related inversely to B.1.427 and B.1.429 (Epsilon) and there was a trend for association with B.1.1.7 (Alpha) lineage.

Conclusion

Deviations from accurate predictive models are useful for investigating potential immune escape of COVID-19 variants at the population level. The B.1.526 and B.1.526.2 lineages likely have a high potential for immune escape and should be designated as variants of concern. The T95I mutation which is present in the B.1.526, B.1.526.2, and B.1.617.2 (Delta) lineages in the US warrants further investigation as a mutation of concern.

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  1. ScreenIT

    SciScore for 10.1101/2021.12.06.21267388: (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
    Python’s Pandas [14] package is used to process the data. 0.2.2 Variants Map: For each state and each month, the top three frequent lineages were selected as the candidates for relative importance analysis based on the cumulative lineages cases.
    Python’s
    suggested: (PyMVPA, RRID:SCR_006099)
    The python package statsmodels [12](12) was used to perform the analysis.
    python
    suggested: (IPython, RRID:SCR_001658)
    All statistical analyses were conducted using R version 4.0.1 (2020-06-06), and figures were produced using the ggplot2 package [16].
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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:
    2.2 Limitations of the Study: The data used in this study are cross-sectional. Ideally we would be able to test whether the B.1.526 lineages were reinfecting the same person after being infected with the original strains. Additionally it would be ideal to have data on vaccination status and type of vaccine in the people infected with viral lineages longitudinally. Unfortunately, these data do not exist or are not accessible. We strongly encourage testing and reporting of this data by hospital systems so that we can more clearly understand the role of the variants on breakthrough cases. Another limitation of the study is delays in reporting sequencing data to databases. Accurate lineage data is available only with a three week delay. This means that we are delayed in understanding important information for policy planning and hospital staffing. Our relative importance analysis while useful in understanding the most dominant lineages will fail to capture variants with rising prevalence or variants that dominate regionally. When a specific variant causes a large number of cases over a country it tends to be the focus of study. However the most dominant variant may not the most dangerous one, geographically located variants like B.1.526 are a cause for concern since they may escape notice due their limited presence. 2.3 Future Directions: In the future we hope to access longitudinal data to more directly assess the effect of lineages and their mutations on breakthrough cases and ...

    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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  2. Version published to 10.1101/2021.12.06.21267388 on medRxiv