TITLE:
Covid-19 in West & East Africa, a Geographical Weighted Regression Exploration with http://mygeoffice.org/
AUTHORS:
Joao Negreiros, Samia Loucif, Mohammed Amin Kuhail, Ahmed Seffah
KEYWORDS:
Covid-19, Statistics, Spatial Analysis, Geographical Weighted Regression, myGeoffice©
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.9 No.9,
September
8,
2021
ABSTRACT: Understanding the dynamics that affect the spread of Covid-19 is critical
for the development of government measures to stop and reverse this nowadays
disease propagation. Like in any epidemiological study, it is essential to
analyze the spatial data to account for the inherent spatial heterogeneity
within the data (spatial autocorrelation). This paper uses Geographically
Weighted Regression (GWR) to identify the factors that influence the outbreak
of Covid-19 in Western and Eastern countries of Africa. The analyses include
traditional linear regression (including descriptive statistics, hierarchical
clustering and correlations were not forgotten either) to reveal the importance
of eight risk factors (population density, median age, aged over 65 years, GDP
per capita, cardiovascular death rates, diabetes prevalence, female
and male smokers) regarding Covid-19 dissemination. It is believed that this is
the first attempt to explore possible causes associated with the spread of the
Covid-19 pandemic in these disadvantage countries, where some intriguing clues
are presented for further research such as the positive relationship between
the financial purchase power of nations and the total number of infected people
or the smoker’s gender impact on Covid-19.