Reproducing the long term predictions from Imperial College CovidSim Report 9

  1. Ken Rice
  2. Ben Wynne
  3. Victoria Martin
  4. Graeme Ackland

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Abstract

1

We present calculations using the CovidSim code which implements the Imperial College individual-based model of the COVID epidemic. Using the parameterization assumed in March 2020, we reproduce the predictions presented to inform UK government policy in March 2020. We find that CovidSim would have given a good forecast of the subsequent data if a higher initial value of R0 had been assumed. We then investigate further the whole trajectory of the epidemic, presenting results not previously published. We find that while prompt interventions are highly effective at reducing peak ICU demand, none of the proposed mitigation strategies reduces the predicted total number of deaths below 200,000. Surprisingly, some interventions such as school closures were predicted to increase the projected total number of deaths.

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    SciScore for 10.1101/2020.06.18.20135004: (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.

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    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.06.18.20135004v1 on medRxiv