Impact of voluntary risk-mitigation behaviour on transmission of the Omicron SARS-CoV-2 variant in England

  1. Ellen Brooks-Pollock
  2. Kate Northstone
  3. Lorenzo Pellis
  4. Francesca Scarabel
  5. Amy Thomas
  6. Emily Nixon
  7. David A. Matthews
  8. Vicky Bowyer
  9. Maria Paz Garcia
  10. Claire J. Steves
  11. Nicholas J. Timpson
  12. Leon Danon

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Abstract

Background

The Omicron variant of SARS-CoV-2 infection poses substantial challenges to public health. In England, “plan B” mitigation measures were introduced in December 2021 including increased home working and face coverings in shops, but stopped short of restrictions on social contacts. The impact of voluntary risk mitigation behaviours on future SARS-CoV-2 burden is unknown.

Methods

We developed a rapid online survey of risk mitigation behaviours during the winter 2021 festive period and deployed in two longitudinal cohort studies in the UK (Avon Longitudinal Study of Parents and Children (ALSPAC) and TwinsUK/Covid Symptom Study (CSS) Biobank) in December 2021. Using an individual-based, probabilistic model of COVID-19 transmission between social contacts with SARS-CoV-2 Omicron variant parameters and realistic vaccine coverage in England, we describe the potential impact of the SARS-CoV-2 Omicron wave in England in terms of the effective reproduction number and cumulative infections, hospital admissions and deaths. Using survey results, we estimated in real-time the impact of voluntary risk mitigation behaviours on the Omicron wave in England, if implemented for the entire epidemic wave.

Results

Over 95% of survey respondents (N ALSPAC =2,686 and N Twins =6,155) reported some risk mitigation behaviours, with vaccination and using home testing kits reported most frequently. Less than half of those respondents reported that their behaviour was due to “plan B”. We estimate that without risk mitigation behaviours, the Omicron variant is consistent with an effective reproduction number between 2.5 and 3.5. Due to the reduced vaccine effectiveness against infection with the Omicron variant, our modelled estimates suggest that between 55% and 60% of the English population could be infected during the current wave, translating into between 15,000 and 46,000 cumulative deaths, depending on assumptions about vaccine effectiveness. We estimate that voluntary risk reduction measures could reduce the effective reproduction number to between 1.8 and 2.2 and reduce the cumulative number of deaths by up to 24%.

Conclusions

We conclude that voluntary measures substantially reduce the projected impact of the SARS-CoV-2 Omicron variant, but that voluntary measures alone would be unlikely to completely control transmission.

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  1. Version published to 10.1101/2022.01.26.22269540v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2022.01.26.22269540: (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
    ALSPAC is an intergenerational prospective birth cohort from the southwest of England.
    ALSPAC
    suggested: (ALSPAC, RRID:SCR_007260)
    The TwinsUK/CSS Biobank survey was implemented in REDCap, accessible via an anonymous link advertised in the Christmas newsletter.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are other limitations in our modelling approach. As the model is not dynamic, we estimate the reproduction number and cumulative number of cases and deaths but cannot estimate the timescale over which cases and deaths occur or the peak burden of infection or deaths. The estimates of cumulative infections, hospital admissions and deaths are based on theoretical results that link the reproduction number to cumulative burden over the entirety of an epidemic. That means that this method intrinsically assumes an unmitigated epidemic, with no changes in behaviour, policy, adherence to guidelines or biological properties of COVID-19. In reality of course, epidemics are rarely unmitigated, especially if they are large. This simplified modelling framework was used to inform decision-making in real-time. Our approach is less computationally intensive than dynamic models and by using theoretical results we provide a fast, transparent, and intuitive understanding of how behaviour translates to hospital admissions and deaths. In contrast to the majority of dynamic models used for forward epidemic projections [30–32], our approach is individual-based where individuals are explicitly modelled and scaled up to the population-level. This means that individual-level survey data can be readily incorporated, along with the associated uncertainties. The main source of uncertainty in our modelled estimates is uncertainty in the severity of disease, both intrinsic severity (the probability of...

    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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  3. Version published to 10.1101/2022.01.26.22269540v1 on medRxiv