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

Abstract

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

Managing health through environmental policies. Analysis for European Union countries

“…Furthermore, the analysis showed (only in Model 1) that the increase in the share of energy taxes in total taxes negatively impacts the health index, which may raise some additional questions as to why, since environmental taxes in relation to GDP have a positive effect in general. This could be surprising since, for example, a study by Hassan et al (2021) presenting the impact of these taxes on physical investment, human capital and environmental innovation found that energy taxes negatively impact polluting emissions. In turn, this should translate into health indicators.…”

Lower COVID‐19 Incidence in Low‐Continentality West‐Coast Areas of Europe

“…JHUM, 2020). Even though mapping of risk or correlations is advancing (e.g., Al‐Kindi et al., 2020; Hassaan et al., 2021) the potential of GIS as a helping tool to understand COVID‐19 is far from being fully utilized (Ahasan & Hossain, 2021). We here integrate GIS and statistics to find both a (visualized) pattern and statistical correlations between COVID‐19 and environmental factors.…”

Bayesian network-based spatial predictive modelling reveals COVID-19 transmission dynamics in Eswatini

“…Early forecasting of the potential outbreaks is key in making effective infection prevention and control strategies [6]. Combating and proper management of COVID-19 spread, therefore requires an understanding of the key determinants of its morbidity [7].…”

“…Mapping disease outbreaks can also assist in understanding their key determinants [7]. Statistical analysis of spatial patterns is more effective as it involves assessing statistical significance of clustering or dispersion and modelling spatial relationships.…”