A Statistical Model for Quantifying the Needed Duration of Social Distancing for the COVID-19 Pandemic

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Abstract

Understanding the effectiveness of strategies such as social distancing is a central question in attempts to control the COVID-19 pandemic. A key unknown in social distancing strategies is the duration of time for which such strategies are needed. Answering this question requires an accurate model of the transmission trajectory. A challenge in fitting such a model is the limited COVID-19 case data available from a given location. To overcome this challenge, we propose fitting a model of SARS-CoV-2 transmission jointly across multiple locations. We apply the model to COVID-19 case data from Spain, UK, Germany, France, Denmark, and New York to estimate the distribution for the time needed for social distancing to end to range from May 2020 to July 2021 (95% credible interval), where the median date is October, 2020. Our method is not specific to COVID-19, and it can also be applied to future pandemics.

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  1. SciScore for 10.1101/2020.05.30.20117796: (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: We detected the following sentences addressing limitations in the study:
    The above limitations need to be taken into account when interpreting our analysis. However, we note that as new data on immunity, seasonality, medications, and vaccines becomes available, these can easily be incorporated into our framework, and a revised analysis can be performed. We provide freely available code that allows for such an analysis by researchers in the community (see appendix). Finally, we would like to point out that the issue of sensitivity of the model to the parameter choices is not specific to the SEIR model. Specifically, in our hands we have observed a similar phenomenon for other models as well (data not shown). The limited availability of data limits the certainty with which parameters of the model can be identified. Thus, it is critical that estimates from the application of statistical models to such data be accompanied by formal measures of uncertainty. We believe that the statements resulting from our analysis should also be taken in the context of the specific locations we analyzed and the specific model that we used. Possibly, other models or other locations may provide different estimates.

    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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