What influences COVID-19 infection rates: A statistical approach to identify promising factors applied to infection data from Germany

  1. Moritz Mercker
  2. Uwe Betzin
  3. Dennis Wilken

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Abstract

The recent COVID-19 pandemic is of big and world-wide concern. There is an intense discussion and uncertainty which factors and sanctions can reduce infection rates. The overall aim is to prevent an overload of the medical system. Even within one country, there is frequently a strong local variability in both – political sanctions as well as other local factors – which may influence infection rates. The main focus of study is analysis and interpretation of recent temporal developments (infection rates). We present a statistical framework designed to identify local factors which reduce infection rates. The approach is robust with respect to the number of undetected infection cases. We apply the framework to spatio-temporal infection data from Germany. In particular, we demonstrate that (1) infection rates are in average significantly decreasing in Germany; (2) there is a high spatial variability of these rates, and (3) both, early emergence of first infections and high local infection densities has led to strong recent decays in infection rates, suggesting that psychological effects (such as awareness of danger) lead to behaviour changes that reduce infection rates. However, the full potential of the presented method cannot yet be exploited, since more precise spatio-temporal data, such as local cell phone-based mobility data, are not yet available. In the nearest future, the presented framework could be applied to data from other countries at any state of infection, even during the exponential phase of the growth of infection rates.

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    SciScore for 10.1101/2020.04.14.20064501: (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
    For LASSO analyses we used the function glmnet() from the R-package glmnet [5], and for all visualizstions we used the R-package ggplot2 [17].
    R-package
    suggested: None
    glmnet
    suggested: (glmnet, RRID:SCR_015505)
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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 10.1101/2020.04.14.20064501v1 on medRxiv