COVID-19 IN INDIA: MODELLING, FORECASTING AND STATE-WISE COMPARISON

  1. Ankitha Jose
  2. Ashid Salim
  3. Silpa Subhash
  4. Noel George

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

COVID-19 has turned the whole world upside down economically and socially. COVID-19 pandemic has caused around five crores of cases and three lakhs deaths globally as of 27 May 2020. This paper adopts four mathematical growth models. Basic models are encouraged because these models can make predictions with the available data and variables in the current scenario of COVID-19 pandemic. The best-fitted model is identified in accordance with the value of the coefficient of determination. As per the best model, there might be greater than 16 lakhs cases at the infection end in India. After predicting the future size of the pandemic, we analyzed how the disease severity varies among the Indian states and union territories using Case Fatality Rates (CFR).

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.06.15.20131375: (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
    Data were analyzed using python 3.8.2 (community version).
    python
    suggested: (IPython, RRID:SCR_001658)

    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 10.1101/2020.06.15.20131375v1 on medRxiv