Analyzing the Global Impact of COVID-19 Vaccination Progress: A Result-oriented Storytelling Approach

  1. Samrat Kumar Dey
  2. Md. Mahbubur Rahman
  3. Umme Raihan Siddiqi
  4. Arpita Howlader
  5. Arifuzzaman Tushar

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Abstract

The next big step in combating the coronavirus disease 2019 (COVID-19) pandemic will be gaining widespread acceptance of a vaccination campaign for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but achieving high uptake need proper understandings. Many health professionals, researchers, statisticians, and programmers to track the viruses spread in different parts of the world have used various methods. However, the proliferation of vaccines produced by talented scientists around the world has sparked a strong desire to extract meaningful insights from available data. Until now, several vaccines against coronavirus disease (COVID-19) have been approved and are being distributed worldwide in various regions. This study aims to report the detailed data analysis and result-oriented storytelling of the COVID-19 vaccination program of different countries across the globe. To analyze the vaccination trend globally this research utilized two different open datasets provided by ourworldindata.org and worldometers.info. An exploratory data analysis (EDA) with interactive data visualization using various python libraries was conducted, and the results are presented in this article to better understand the impact of ongoing vaccination programs around the world. Apart from the valuable insights gained from the data of various countries, this investigation also included a comparison of the number of confirmed and death cases before and after vaccination to determine the efficacy of each vaccine in each country. The results show that a large number of people are still undecided about whether or not to get a COVID-19 vaccine, despite the virus’s continued devastating effects on communities. Overall, our findings contribute to ongoing research aimed at informing policy on how to persuade the unvaccinated to be vaccinated.

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    SciScore for 10.1101/2021.03.26.21254432: (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
    For data ingestion, visualization and analysis purpose we initialized different python packages including NumPy (https://numpy.org/), Pandas (https://pandas.pydata.org/), Matplotlib (https://matplotlib.org/), Seaborn (https://seaborn.pydata.org/), and Plotly (https://plotly.com/).
    python
    suggested: (IPython, RRID:SCR_001658)
    NumPy
    suggested: (NumPy, RRID:SCR_008633)
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    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.

    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 10.1101/2021.03.26.21254432v1 on medRxiv