Is the impact of social distancing on coronavirus growth rates effective across different settings? A non-parametric and local regression approach to test and compare the growth rate

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Abstract

Epidemiologists use mathematical models to predict epidemic trends, and these results are inherently uncertain when parameters are unknown or changing. In other contexts, such as climate, modellers use multi-model ensembles to inform their decision-making: when forecasts align, modellers can be more certain. This paper looks at a sub-set of alternative epidemiological models that focus on the growth rate, and it cautions against relying on the method proposed in (Pike & Saini, 2020): relying on the data for China to calculate future trajectories is likely to be subject to overfitting, a common problem in financial and economic modelling. This paper finds, surprisingly, that the data for China are double-exponential, not exponential; and that different countries are showing a range of different trajectories. The paper proposes using non-parametric and local regression methods to support epidemiologists and policymakers in assessing the relative effectiveness of social distancing policies. All works contained herein are provided free to use worldwide by the author under CC BY 2.0 .

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  1. SciScore for 10.1101/2020.04.07.20049049: (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: 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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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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