COVID-19 pandemic: Impact of lockdown, contact and non-contact transmissions on infection dynamics

  1. Shovonlal Roy

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Abstract

COVID-19 coronavirus pandemic has virtually locked down the entire world of human population, and through its rapid and unstoppable spread COVID-19 has essentially compartmentalised the population merely into susceptible, exposed, infected and recovered classes. Adapting the classical epidemic modelling framework, two distinct routes of COVID-19 transmission are incorporated into a model: (a) direct person-to-person contact transmission, and (b) indirect airborne and fomites-driven transmission. The indirect non-contact transmission route needs to explored in models of COVID-19 spread, because evidences show that this route of transmission is entirely viable with hugely uncertain level of relative contribution. This theoretical study based on model simulations demonstrates the following: (1) Not incorporating indirect transmission route in the model leads to underestimation of the basic reproduction number, and hence will impact on the COVID-19 mitigation decisions; (2) Lockdown measures can suppress the primary infection peak, but will lead to a secondary peak whose relative strength and time of occurrence depend on the success and duration of the lockdown measures; (3) To make lockdown effective, a considerable level of reduction in both contact and non-contact transmission rates over a long period is required; (4) To bring down the infection cases below any hypothetical health-care capacity, reduction of non-contact transmission rate is key, and hence active measures should be taken to reduce non-contact transmission (e.g., extensive uses of areal and aerosol disinfectant in public spaces to improve contaminated surfaces and air); (5) Any premature withdrawal of lockdown following the sign of a brief retracement in the infection cases can backfire, and can lead to a quicker, sharper and higher secondary peak, due to reactivation of the two transmission routes. Based on these results, this study recommends that any exit policy from lockdown, should take into account the level of transmission reduction in both routes, the absolute scale of which will vary among countries depending on their health-service capacity, but should be computed using accurate time-series data on infection cases and transmission rates.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.04.20050328: (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

    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. ScreenIT

    SciScore for 10.1101/2020.04.04.20050328: (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

    Software and Algorithms
    SentencesResources
    All simulations are done using MATLAB R2016b package.
    MATLAB
    suggested: (MATLAB, SCR_001622)

    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).

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, please follow this link.

  3. Version published to 10.1101/2020.04.04.20050328v1 on medRxiv