The connection of growth and medication of COVID-19 affected people after 30 days of lock down in India

  1. Achintya Bhattacharyya
  2. Debosmita Bhattacharyya
  3. Joy Mukherjee

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Abstract

The COVID-19 pandemic has already consumed few months of indolence all over the world. Almost every part of the world from which the victim of COVID 19 are, have not yet been able to find out a strong way to combat corona virus. Therefore, the main aim is to minimize the spreading of the COVID-19 by detecting most of the affected people during lockdown. Hence, it is necessary to understand what the nature of growth is of spreading of this corona virus with time after almost one month (30 days) of lockdown. In this paper we have developed a very simple mathematical model to describe the growth of spreading of corona virus in human being. This model is based on realistic fact and the statistics we have so far. For controlling the spread of the COVID-19, minimization of the growth with minimum number of days of lockdown is necessary. We have established a relation between the long-term recovery coefficient and the long-term infected coefficient. The growth can be minimized if such condition satisfies. We have also discussed how the different age of the people can be cured by applying different types of medicine. We have presented the data of new cases, recovery and deaths per day to visualize the different coefficient for India and establish our theory. We have also explained how the medicine could be effective to sustain and improve such condition for country having large population like India.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.21.20107946: (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

    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.

    About SciScore

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  2. Version published to 10.1101/2020.05.21.20107946v1 on medRxiv