Fourth Wave of COVID-19 in India : Statistical Forecasting

  1. Sabara Parshad Rajeshbhai
  2. Subhra Sankar Dhar
  3. Shalabh

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Abstract

The spread of COVID-19 pandemic has wave nature. This article proposes a statistical methodology to study and forecast the future waves. The methodology is applied to COVID-19 data from India to statistically forecast the occurrence of fourth wave in India. In the course of this study, the data is fitted by the mixture of Gaussian distribution, and Bootstrap methodology is used to compute the confidence interval of the time point of peak of the fourth wave. This methodology can also be used to forecast the fourth and other waves in other countries also.

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    SciScore for 10.1101/2022.02.23.22271382: (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 the analysis presented in this paper, the actual sub-data set used is available at https://home.iitk.ac.in/~shalab/covid-paper-data/Covid4wave/filtered_data_Covid4wave.csv, and the python code used for the analysis is attached as a supplementary file “code.pdf”.
    python
    suggested: (IPython, RRID:SCR_001658)
    To perform it, Gaussian Mixture object of scikit-learn package of python [4] was used.
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)

    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 10.1101/2022.02.23.22271382v1 on medRxiv