Statistical Inferences and Analysis based on the COVID-19 Data from the United States

  1. Nivedita Rethnakar

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Abstract

This paper investigates the mortality statistics of the COVID-19 pandemic from the United States perspective. We bring out several exciting and glaring aspects of the pandemic, otherwise hidden, using empirical data analysis and statistical inference tools. First, specific patterns seen in demographics such as race/ethnicity are analyzed qualitatively and quantitatively. We looked at the role of factors such as population density in mortality rates. A detailed study of the connections between COVID-19 and other respiratory diseases is also covered. Finally, we examine the temporal dynamics of the COVID-19 outbreak and vaccines’ stellar impact in controlling the pandemic. Statistical inference such as the ones gathered in this paper would be helpful for better scientific understanding, policy preparation, and thus adequately preparing, should a similar situation arise in the future.

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  1. ScreenIT

    SciScore for 10.1101/2021.11.30.21267096: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  2. Version published to 10.1101/2021.11.30.21267096 on medRxiv