A Net Benefit Approach for the Optimal Allocation of a COVID-19 Vaccine

  1. Erin Kirwin
  2. Ellen Rafferty
  3. Kate Harback
  4. Jeff Round
  5. Christopher McCabe

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Abstract

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  1. Version published to 10.1007/s40273-021-01037-2
  2. Version published to 10.1101/2020.11.30.20240986v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.11.30.20240986: (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
    CRAM estimates epidemiologic outcomes such as active cases, hospitalisations, and total infections over time, and produces health economic outputs including total cost, QALYs, and NMB.
    CRAM
    suggested: (CRAM, RRID:SCR_012975)
    Cost and disutility estimates are presented as incremental values in Table 2.
    Cost
    suggested: (COST, RRID:SCR_014098)

    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:
    Limitations: A key challenge for all COVID-19 modelling and simulation studies has been data quality. The challenge is two-fold: model inputs and parameters (such as the duration of infection and transmissibility by age group) are uncertain and estimated from small observational studies. In addition, jurisdictional observational data are subject to bias. The high degree of uncertainty in many of the model elements necessitates constant review and reappraisal of the model structure and inputs. Constant evolution of the evidence also highlights the importance of the stochastic approach used by this study. By sampling from the distributions of several parameters, the stochastic results quantify the resulting uncertainty in model outputs. Seasonal dynamics related to COVID-19 remain controversial. Several studies have found empirical and modelling evidence to support changes in virus transmission consistent with seasonal behaviour, and these seasonal changes in transmission can be driven by both behavioural and environmental factors. A review on the stability of coronaviruses including COVID-19 under various environmental and climatic conditions [37] found early evidence of virus sensitivity to temperature, light, and humidity in laboratory studies [38-42]. Many studies analysing the natural history of the virus imply the presence of seasonal dynamics [43-50], while others found a limited effect [51, 52]. Natural history studies are challenged with data quality issues, confoundin...

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.11.30.20240986v1 on medRxiv