GIS-Based Analysis Framework for identifying COVID-19 Incidence and Fatality Determinants at National Level Case study: Africa
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 16thof 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.Pvalue <0.1), and fatality (Moran index 0.0.35,Pvalue<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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