UTTARAKHAND COVID-19 CASES AND DEATHS: COMPARING THE DEMOGRAPHICS AND SUGGESTIVE STRATEGY TO PREVENT SIMILAR PANDEMICS IN FUTURE

  1. Gaurav Joshi
  2. Akshara Pande
  3. Omdeep Gupta
  4. Anoop Nautiyal
  5. Sanjay Jasola
  6. Prashant Gahtori

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Abstract

Coronavirus disease 19 (Covid-19) is causing a dramatic impact on human life worldwide. As of June 11 2021, later one has attributed more than 174 million confirmed cases and over 3.5 million deaths globally. Nonetheless, a World Bank Group flagship report features Covid-19 induced global crisis as the strongest post-recession since World WarII. Currently, all approved therapeutics or vaccines are strictly allowed for emergency use. Hence, in the absence of pharmaceutical interventions, it is vital to analyze data set covering the growth rates of positive human cases, number of recoveries, other factors, and future strategies to manage the growth of fatal Covid-19 effectively. The Uttarakhand state of India is snuggled in the lap of the Himalayas and occupies more people than Israel, Switzerland, Hong Kong, etc. This study analyzed state Covid-19 data, fetched from an authenticated government repository using Python 3.9 from April 1, 2020, to February 28, 2021. In the first wave, plain areas of Uttarakhand covering the districts Dehradun, Haridwar, Nainital, and U. S. Nagar were severely affected and reported peak positive cases during the 21st – 26th week. Other hands, the hilly terrains of Uttarakhand districts, including Chamoli, Pauri Garhwal, and Rudraprayag, reported a high number of positive cases between the 30th and 31st week, and other hilly districts reported an increase in Covid-19 cases during the 34th to 38th week. The highest recovery rate was attributed to the hilly district Rudraprayag. The analysis also revealed that a very high doubling rate was seen during the last week of May to the first week of Jun 2020. At last, based on this blueprint, we have suggested 6-points solutions for preventing the next pandemic.

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    SciScore for 10.1101/2021.09.03.21263064: (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

    Software and Algorithms
    SentencesResources
    Python command-line utility pdf2txt.py was used to extract text contents and stored them in CSVfile format. 2.3. Information Extraction: All.csv files were converted to .xls format for analytical purposes.
    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.

    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 10.1101/2021.09.03.21263064v1 on medRxiv