Corona virus diseases 2019 (COVID-19) pandemic is an extraordinary threat with significant implications in all aspects of human life; therefore, it represents the most immediate challenge for the countries all over the world. This study, hence, is intended to identify the best GIS-based model that can explore, quantify, and model the determinants of COVID-19 incidence and fatality. For this purpose, geospatial models were developed to estimate COVID-19 incidence and fatality rates in Africa, up to 16th of August 2020 at the national level. The models involved Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) analysis using ArcGIS. Spatial autocorrelation analysis recorded a positive spatial autocorrelation in COVID-19 incidence (Moran index 0.16, P = 0.1) and fatality (Moran index 0.26, P = 0.01) rates within different African countries. GWR model had higher R2 than OLS for prediction of incidence and mortality (58% vs 45% and 55% vs 53%). The main predictors of COVID-19 incidence rate were overcrowding, health expenditure, HIV infections, air pollution, and BCG vaccination (mean β = 3.10, 1.66, 0.01, 3.79, and −66.60 respectively, P < 0.05). The main determinants of COVID-19 fatality were prevalence of bronchial asthma, tobacco use, poverty, aging, and cardiovascular diseases fatality (mean β = 0.00162, 0.00004, −0.00025, −0.00144, and −0.00027 respectively, P < 0.05). Application of the suggested model can assist in guiding intervention strategies, particularly at the local and community level whenever the data on COVID-19 cases and predictors variables are available.
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.
Egypt is a distinctive country in terms of its rich and unique tangible cultural heritage including monuments and archaeological sites distributed across the country. Many monuments and archaeological sites are facing a variety of climate change‐associated hazards with a wide range of cross‐sectoral impacts. This research intends to identify climate change‐associated hazards on tangible cultural heritage in Egypt, highlighting the main areas of concern. For this purpose, a Geographic Information System (GIS)‐based methodology is utilized, beginning with defining a framework for hazard identification. This is followed by developing a geospatial database of tangible cultural heritage. Meanwhile, expected changes in relevant climate parameters under Representative Concentration Pathway (RCP) 8.5 scenario up to 2065 were profiled. Thereafter, a geospatial database of existing tangible cultural heritage in Egypt as well as current and future climate parameters are employed to examine the exposure of archaeological heritage in Egypt to various climate change‐associated hazards up to the year 2065. It was found that the tangible cultural heritage sites in Egypt, accounting for 205 sites, are exposed to specific or combined levels of climate change‐associated hazards depending on their geographic settings. In this respect, it was found that 25% of archaeological sites in Egypt are susceptible to combined high to moderate temperature ranges and humid conditions as a result of climate change up to 2065. This highlights the need for developing archaeological site conservation strategies based not only on current conservation needs, including anthropogenic and environmental stressors, but also on climate change‐associated hazards. Such a strategy needs to prioritize different cultural heritage assets actions according to their uniqueness as well as associated direct and indirect benefits.
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