COVID-19 Lockdowns May Reduce Resistance Genes Diversity in the Human Microbiome and the Need for Antibiotics

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Recently, much attention has been paid to the COVID-19 pandemic. Yet bacterial resistance to antibiotics remains a serious and unresolved public health problem that kills hundreds of thousands of people annually, being an insidious and silent pandemic. To contain the spreading of the SARS-CoV-2 virus, populations confined and tightened hygiene measures. We performed this study with computer simulations and by using mobility data of mobile phones from Google in the region of Lisbon, Portugal, comprising 3.7 million people during two different lockdown periods, scenarios of 40 and 60% mobility reduction. In the simulations, we assumed that the network of physical contact between people is that of a small world and computed the antibiotic resistance in human microbiomes after 180 days in the simulation. Our simulations show that reducing human contacts drives a reduction in the diversity of antibiotic resistance genes in human microbiomes. Kruskal–Wallis and Dunn’s pairwise tests show very strong evidence (p < 0.000, adjusted using the Bonferroni correction) of a difference between the four confinement regimes. The proportion of variability in the ranked dependent variable accounted for by the confinement variable was η2 = 0.148, indicating a large effect of confinement on the diversity of antibiotic resistance. We have shown that confinement and hygienic measures, in addition to reducing the spread of pathogenic bacteria in a human network, also reduce resistance and the need to use antibiotics.

Article activity feed

  1. SciScore for 10.1101/2021.03.08.21253164: (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.
    • Thank you for including a protocol registration statement.

    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.