Influence of the Demographic, Social, and Environmental Factors on the Covid-19 Pandemic—Analysis of the Local Variations Using Geographically Weighted Regression

Datos Bibliográficos

ID15511124
AutoresKrzysztof Rząsa (0000-0001-5215-7630, University of Warmia and Mazury in Olsztyn), Mateusz Ciski (0000-0002-4919-9570, University of Warmia and Mazury in Olsztyn, autor de correspondencia)
Año2022
Volumen19
Número19
Páginas11881-11881
Fecha de publicación2022-09-20
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaInternational Journal of Environmental Research and Public Health (JOURNAL)
Identificadores de la revistaISSN: 1661-7827 • E-ISSN: 1660-4601
EditorialMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph191911881
PMID36231184
OpenAlexW4296745925
IdiomaEN
Citas recibidas2
Referencias citadas105

As the COVID-19 pandemic continues, an increasing number of different research studies focusing on various aspects of the pandemic are emerging. Most of the studies focus on the medical aspects of the pandemic, as well as on the impact of COVID-19 on various areas of life; less emphasis is put on analyzing the influence of socio-environmental factors on the spread of the pandemic. In this paper, using the geographically weighted regression method, the extent to which demographic, social, and environmental factors explain the number of cases of SARS-CoV-2 is explored. The research was performed for the case-study area of Poland, considering the administrative division of the country into counties. The results showed that the demographic factors best explained the number of cases of SARS-CoV-2; the social factors explained it to a medium degree; and the environmental factors explained it to the lowest degree. Urban population and the associated higher amount and intensity of human contact are the most influential factors in the development of the COVID-19 pandemic. The analysis of the factors related to the areas burdened by social problems resulting primarily from the economic exclusion revealed that poverty-burdened areas are highly vulnerable to the development of the COVID-19 pandemic. Using maps of the local R 2 it was possible to visualize how the relationships between the explanatory variables (for this research-demographic, social, and environmental factors) and the dependent variable (number of cases of SARS-CoV-2) vary across the study area. Through the GWR method, counties were identified as particularly vulnerable to the pandemic because of the problem of economic exclusion. Considering that the COVID-19 pandemic is still ongoing, the results obtained may be useful for local authorities in developing strategies to counter the pandemic

Coronavirus disease 2019 (COVID-19 · Economic growth · Economics · Geography · Pandemic · Population · Poverty · Regression analysis · Social distance · Socioeconomics · Sociology · Statistics · COVID-19 epidemiological studies · COVID-19 impact on air quality · COVID-19 Pandemic Impacts · Demography · Medicine