Covid-19 pandemic Analysis using Regression

  1. P Raji
  2. GR Deeba Lakshmi

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Abstract

Covid-19 is a pandemic which has affected all parts of the world. Covid-19 is a pandemic which can be controlled only by maintaining social distancing, proper hygiene, wearing mask, hand sanitation and to a extend by wearing face shield. Even though each state has followed their own ways of controlling the infection, awareness among citizens and behaving as responsible citizens is very important in controlling this disease. Contact tracing plays an important role in controlling this pandemic. This paper deals with the effect of Covid-19 in various states of India and also forecasts its effect using machine learning techniques. Regression analysis like Linear and polynomial have been used for analysis of Covid-19, where Kaggle dataset has been used. This helps in understanding the much-affected states in India and helps to understand the infrastructure requirements to handle this pandemic efficiently.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.08.20208991: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Dataset has been taken from Kaggle website from 2nd March 2020 to 28th July 2020.
    Kaggle
    suggested: (Kaggle, RRID:SCR_013852)
    Linear regression analysis and polynomial regression analysis has been modelled using Matlab software and effect of Covid-19 has been analysed.A relationship between variables are modelled using regression analysis.
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    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 10.1101/2020.10.08.20208991v1 on medRxiv