GIS-Based Analysis Framework for identifying COVID-19 Incidence and Fatality Determinants at National Level Case study: Africa

  1. Mahmoud A. Hassan
  2. Ramy Mohamed Ghazy
  3. Rofida G. Abdelwahab
  4. Toka A. Elbarky

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Abstract

Background

COVID-19 pandemic is an extraordinary threat with significant implications in all aspects of human life, therefore, it represents the most immediate challenges for all countries all over the world.

Objectives

This study is intended to develop a GIS-based analysis model to explore, quantify and model the relationships between COVID-19 morbidity and mortality and their potential predictor variables.

Method

For this purpose, a model was developed to estimate COVID-19 incidence and fatality rates in Africa up to 16 th of August 2020 at the national level. The model involved Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) analysis through ArcGIS was applied.

Result

Spatial Autocorrelation Analysis revealed that there was positive spatial autocorrelation in COVID-19 incidence (Moran index 0.16. P value <0.1), and fatality (Moran index 0.0.35, P value<0.01) rates within different African countries. At continental level, OLS revealed that COVID-19 incidence rate was found to be positively associated with overcrowding, health expenditure, HIV infections and air pollution and negatively associated with BCG vaccine (β=2.97,1.45, 0.01, 3.29, −47.65 respectively, P < 0.05) At the same time, COVID-19 fatality was found to be positively related to asthma prevalence and tobacco use. Yet, certain level of inconsistency was noted in the case of COVID-19 fatality, which was negatively related to elder population, poverty, and cardiovascular mortality ( P <0.05). This model showed convenient level of validity in modeling the relationship between COVID-19 incidence as well as fatality and their key predictors using GWR. In this respect, the model explained about 58% and 55% of the variance in COVID-19 incidence and fatality rates, respectively, as a function of considered predictors.

Conclusion

Application of the suggested model can assist in guiding intervention strategies, particularly in case of local and community level whenever the data on COVID-19 cases and predictors variables are available.

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  1. Version published to 10.1101/2021.01.12.21249661v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2021.01.12.21249661: (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.

    About SciScore

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  3. Version published to 10.1101/2021.01.12.21249661v1 on medRxiv
  4. Version published to 10.1177/21501327211041208