Impacts of people’s learning behavior in fighting the COVID-19 epidemic

  1. Baolian Cheng
  2. Yi-Ming Wang

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Abstract

This work presents a mathematical model that captures time-dependent social-distancing effects and presents examples of the consequences of relaxing social-distancing restrictions in the fight against the novel coronavirus epidemic. Without social distancing, the spread of COVID-19 will grow exponentially, but social distancing and people’s learning behavior (isolating, staying at home, wearing face masks, washing hands, restricting the size and frequency of group gatherings, etc.) can significantly impede the epidemic spread, flatten the infection curve, and change the final outcome of the COVID-19 outbreak. Our results demonstrate that strict social distancing and people’s learning behavior can be effective in slowing the spread rate and significantly reducing the total number of infections, daily infection rate, peak of daily infections, and duration of the epidemic. Under strict social distancing, the rise and fall of infections would be nearly symmetric about the peak of of daily infections, and the epidemic spread would be essentially over within 60 days. Relaxing social distancing and people learning behaviors will significantly increase the total and daily numbers of infections and prolong the course of the outbreak. These results have immediate applications for the implementation of various social-distancing policies and general significance for ongoing outbreaks and similar infectious disease epidemics in the future (LA-UR 20-22877).

This material is not final and is subject to be updated any time. Contact information: bcheng@lanl.gov .)

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  1. ScreenIT

    SciScore for 10.1101/2020.08.02.20166967: (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.

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  2. Version published to 10.1101/2020.08.02.20166967v1 on medRxiv