Epidemiology study of SARS-CoV-2 pandemic in India, the first and second wave

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Abstract

Background and objectives

SARS-CoV-2 has wrecked the world for the past 17 months. India has been hit by the second wave of the virus which has been characterized by new symptoms. This study focuses on the pattern of infection over the last 13 months utilizing epidemic model to predict course of the pandemic.

Material and methods

The data was collected from covid19india.org to perform analysis based on age and gender distribution. Statistical analysis was performed to determine the relation between confirmed and recovered cases while SIR epidemic model was used to determine the course of the pandemic in the country and the changes that have occurred from the first to the second wave.

Results and discussions

Results show infectivity rate to be higher in ages 20-50 while mortality is higher in 50-80 age group while 60-70% of the infected population are males. Each of the 9 states have their own salient feature curves of infection. It was seen that the confirmed and recovered cases are more correlated at present than previous wave. The curves for both waves show a polynomial distribution while the reproduction number data shows an almost U-shaped curve indicating decrease of infection spread in the middle phase when the first wave was on a decline before picking up again owing to the second wave.

Interpretations and conclusion

The gender and age distribution shows that although lower age group is more infected, mortality is high for higher age groups, on the other hand males are more prone to the infection. The statistical analysis shows the nature of spread of the disease, the data of which is quantified by the SIR model based study.

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  1. SciScore for 10.1101/2021.12.20.21268016: (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

    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.