Identification of risk factors contributing to COVID-19 incidence rates in Bangladesh: A GIS-based spatial modeling approach

  1. Md. Hamidur Rahman
  2. Niaz Mahmud Zafri
  3. Fajle Rabbi Ashik
  4. Md Waliullah
  5. Asif Khan

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Abstract

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  1. Version published to 10.1016/j.heliyon.2021.e06260
  2. ScreenIT

    SciScore for 10.1101/2020.08.16.20175976: (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

    Software and Algorithms
    SentencesResources
    OLS models were developed in ArcGIS software.
    ArcGIS
    suggested: (ArcGIS for Desktop Basic, RRID:SCR_011081)

    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:
    One of the limitations of our study was data availability. Due to unavailability of individual or community level data, it is not logical to draw inferences at individual or community level. Another instance, weather data for 64 districts were interpolated from 28 station data in this study. The detailed weather related data availability might change the findings of the model. Another reservation is that spatial availability of COVID-19 testing center: in Bangladesh, there are few opportunities to test COVID-19 for people living in the remote area. Besides, there is a tendency among people not to test COVID-19 even though they have symptoms. Therefore, there might be an underestimation of COVID-19 cases. Furthermore, the influence of lockdown and other containment measures on COVID-19 incidence rates was not considered in this study. There is obvious to have variations in lockdown related policies and their implementation efficiency within a district. It might play an important role in the district-level to control the COVID-19 incidence rates but analyzing this influence would be out of the scope of this research.

    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 10.1101/2020.08.16.20175976v1 on medRxiv