Fine-scale variation in the effect of national border on COVID-19 spread: A case study of the Saxon-Czech border region

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

No abstract available

Article activity feed

  1. SciScore for 10.1101/2022.03.01.22271644: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot 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: We detected the following sentences addressing limitations in the study:
    A limitation of our study is the lack of direct consideration of some of the surrounding regions, which may have influenced COVID-19 dynamics near the Saxon-Czech border, notably Poland to the far east and the German states of Bavaria and Thuringia to the west. The unavailability of municipality-scale data from these regions precluded their consideration in our analysis. This issue manifests itself in the lower pseudo R2 values for the models and higher p-values on the border term that tend to occur on the outer edges of our study area. This is specifically relevant for the regions of Görlitz in the east and Cheb / Plauen in the west. Outside of these outlying regions, we are confident that our analysis accurately quantifies disease import potential from surrounding regions and provides reliable results. This robustness is further evidenced by the agreement between our core model-based analysis and the confirmatory analyses based on timing differences and on Granger causality. Our index of virus import is based on a comparison of the case increase in a focal municipality with the relative number of cases in the neighboring municipalities one week earlier. Calculation of this index therefore requires determining appropriate values for neighborhood size, time lag, and relative weightings of contributions with the neighborhood. While our selection of these parameters was guided by literature review and knowledge gained via exploratory analysis, we tested the robustness of our re...

    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.

    Results from scite Reference Check: We found no unreliable references.


    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.