Understanding the First and the Second Waves of the COVID-19 in Germany: Is our Social Behavior Enough to Protect us from the Pandemic?

  1. Muhamad KHAIRULBAHRI

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Like other European countries, Germany has experienced the 2nd wave of the COVID-19 amid obligations of social distancing and wearing of face masks in public spaces. Although Germany successfully contained the virus during the 1st wave, it has faced difficulties in controlling the COVID-19 during the 2nd wave. This study develops a computer model representing the COVID-19 flow in Germany by comparing the effects of the measures taken during the 1st and the 2nd waves. The computer model is based on the SEIR concept and the system dynamics (SD) approach in which some unknown parameters are estimated through the calibration process. Moreover, the SEIR computer model is developed by considering different cases in older and young people and the SEIR model successfully reproduces similar patterns of infected, recovered, and death cases in the 1st and the 2nd waves in Germany. The SEIR model also shows that the measures taken in the 1st wave have different efficacies than those in the 2nd wave, leading to higher infected cases during the 2nd wave. Since the SEIR model can successfully reproduce similar patterns, the SEIR model can be a basis for further studies in estimating other resource needs such as health workers, and bed capacities. HIGHLIGHTS The SEIR model estimates the efficacies of behavioral measures and lockdowns Behavioral measures are less effective than lockdowns Germany experienced higher infected cases in the first wave than in the second wave Relaxed lockdowns lead to higher infected cases in the second wave Lockdowns are the key to curb COVID-19 flow GRAPHICAL ABSTRACT

Article activity feed

  1. Version published to 10.48048/wjst.2021.22203
  2. ScreenIT

    SciScore for 10.1101/2020.12.21.20248605: (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

    No key resources detected.

    Results from OddPub: Thank you for sharing your data.

    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.

  3. Version published to 10.1101/2020.12.21.20248605v2 on medRxiv
  4. Version published to 10.1101/2020.12.21.20248605v1 on medRxiv