Implicit, Intrinsic, Extrinsic (or Environmental), and Host Factors Attributing the Covid-19 Pandemic. Part 2-Implicit Factor Pesticide Use: A Systematic Analysis

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Abstract

Advances in our understanding of complex COVID-19 pandemic would allow us to effectively eliminate and eradicate SARS-COV2 virus. Although tremendous amount of research devoted to the robustness across its biology, diagnostics, vaccines and treatment has exploded in the past two years. However, still science do not have robust answers for causes, for example (i) What are the reasons of non-uniform global distribution of COVID-19? (ii) Why the United States, India, and Brazil, are the first-three most affected nations?, (iii) How did Bhutan, a nation sharing a boundary with China manage nearly 0.34% infections and 3 deaths from COVID-19? Nonetheless, the biomass bistribution of biosphere report suggest more than 1550-fold larger microbial biomass involving bacteria, fungi, archaea, protists and viruses is exist in comparision to all global human population in the biosphere. The rich microbiota act a first line of defence to invade pathogens and affect us both through the environment and microbiome. Unfortunately, a role of pathogen-transmission factors viz . implicit factors (competitive microflora) is still under represented. This study is an attempt from a gold standard correlation methodology using a large pesticide use global data. The non-specific pesticides kill both pests as well as protective microbiota, resulting a loss in rich biodiversity and allow easy pathogen entry to human. Entire predictions were found consistent with the recently observed evidences. These insights enhanced scientific ability to interrogate viral epidemiology and recommended to limit pesticide use for future pandemic prevention.

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  1. SciScore for 10.1101/2021.09.09.21263347: (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.
    RandomizationHere, the Countries/ Territories with missing entries were omitted, and 98 countries randomly fitted to top variable indices of correlation coefficient for unbiased cause-effect relationships.
    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.