Modelling the effect of COVID-19 mass vaccination on acute admissions in a major English healthcare system

  1. RD Booton
  2. AL Powell
  3. KME Turner
  4. RM Wood

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Abstract

Background

Managing high levels of severe COVID-19 in the acute setting can impact upon the quality of care provided to both affected patients and those requiring other hospital services. Mass vaccination has offered a route to reduce societal restrictions while protecting hospitals from being overwhelmed. Yet, early in the mass vaccination effort, the possible effect on future bed pressures remained subject to considerable uncertainty. This paper provides an account of how, in one healthcare system, operational decision-making and bed planning was supported through modelling the effect of a range of vaccination scenarios on future COVID-19 admissions.

Methods

An epidemiological model of the Susceptible-Exposed-Infectious-Recovered (SEIR) type was fitted to local data for the one-million resident healthcare system located in South West England. Model parameters and vaccination scenarios were calibrated through a system-wide multi-disciplinary working group, comprising public health intelligence specialists, healthcare planners, epidemiologists, and academics. From 4 March 2021 (the time of the study), scenarios assumed incremental relaxations to societal restrictions according to the envisaged UK Government timeline, with all restrictions to be removed by 21 June 2021.

Results

Achieving 95% vaccine uptake in adults by 31 July 2021 would not avert a third wave in autumn 2021 but would produce a median peak bed requirement approximately 6% (IQR: 1% to 24%) of that experienced during the second wave (January 2021). A two-month delay in vaccine rollout would lead to significantly higher peak bed occupancy, at 66% (11% to 146%) of that of the second wave. If only 75% uptake was achieved (the amount typically associated with vaccination campaigns) then the second wave peak for acute and intensive care beds would be exceeded by 4% and 19% respectively, an amount which would seriously pressure hospital capacity.

Conclusion

Modelling provided support to senior managers in setting the number of acute and intensive care beds to make available for COVID-19 patients, as well as highlighting the importance of public health in promoting high vaccine uptake among the population. Forecast accuracy has since been supported by actual data collected following the analysis, with observed peak bed occupancy falling comfortably within the inter-quartile range of modelled projections.

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    SciScore for 10.1101/2021.10.10.21264821: (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: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and limitations: Modelling future COVID-19 bed demand has been useful in supporting a range of operational management decisions within the BNSSG system, such as opening additional acute infection wards and procuring downstream capacity in the community (given that approximately one-fifth of BNSSG emergency acute patients require ‘step-down’ care upon discharge). Results informed such considerations within the short term, as well as others of a more strategic nature – for instance, the ability to work through the elective backlog at times of low COVID-19 bed demand, without fear of being overwhelmed. Results also informed the public health message to the BNSSG population, in stressing the importance of high vaccine uptake to avoid severe pressure on local hospitals. Turning to limitations, it should be noted that this study did not consider any future SARS-CoV-2 variant nor assume that immunity may wane over time. While, at the time of the study, it was known that neither should be considered unlikely [18,19], there was a deficit of data required to obtain a reliable calibration. Also, due to a lack of credible data, it was not possible to model multiple vaccine doses and so it was assumed that the full benefits of vaccination derive from the first inoculation (this may have been a particular limitation given that the UK was, at the time of the study, applying a 12- week interval between first and second dose [20]). Interpretation within the context of the wider lite...

    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 10.1101/2021.10.10.21264821v1 on medRxiv