Characterizing parametric differences between the two waves of COVID-19 in India

  1. Arpit Omprakash

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The first case of COVID-19 in India was reported on January 30, 2020 [1]. The number of infections rose steeply and preventative measures such as lockdowns were implemented to slow down the spread of the disease. Infections peaked around mid-September the same year and the cases gradually started declining. Following the relaxation of lockdown and the appearance of mutant strains of the virus, a much severe second wave of COVID-19 emerged starting mid-February. For characterization and comparison of both the waves, a SIQR (Susceptible-Infected-Quarantined-Removed) model is used in this paper. The results indicate that a single patient can infect approximately 2.44 individuals in the population. The epidemic doubling time was calculated to be 11.8 days. It is predicted that the actual number of infected patients is grossly underestimated (by a factor of 16) by current testing methods.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.06.29.21259698: (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: 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.

    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 10.1101/2021.06.29.21259698v1 on medRxiv