How and When to End the COVID-19 Lockdown: An Optimization Approach

  1. Thomas Rawson
  2. Tom Brewer
  3. Dessislava Veltcheva
  4. Chris Huntingford
  5. Michael B. Bonsall

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Abstract

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  1. Version published to 10.3389/fpubh.2020.00262
  2. ScreenIT

    SciScore for 10.1101/2020.04.29.20084517: (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
    All code used to perform these optimal control approaches was performed in Matlab, and is available at: https://osf.io/hrt2k/.
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Whilst we believe that our model framework does have predictive capability, we do raise a couple of caveats. We recommended exploring our findings within a variety of other model frameworks. Stochastic frameworks may be better suited to model the exact time periods when populations are first reintroduced, so as to better calculate the range of time frames until a second wave of infections in a probabilistic setting. Likewise compartmental infection models such as those presented by Giordano et al. (2020)9 will be able to provide more accurate estimations on any expected hospital intake. In preparing to monitor the situation upon easing lockdown measures, our sensitivity analysis highlights that the recovery rate of the disease, γ above, is the most critical parameter in understanding the magnitude of any subsequent peaks in infection. Our calculations can be trusted further if that value is well-understood. For example, if new hospitalised patients of COVID-19 appeared to be remaining symptomatic and infectious for longer that previously estimated, it is plausible to assume within the general community that the disease is therefore being transmitted faster than previously expected. This knowledge could trigger preparations for a potential need to reinstate lockdown measures. Hence further research efforts into the infectious period should also therefore be prioritised. In a similar vain, the parameter to which results are second-most sensitive is transmission rate, β, and so ...

    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.

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  3. Version published to 10.1101/2020.04.29.20084517v1 on medRxiv