Assessing the significance of socioeconomic and demographic factors on COVID-19 cases in Turkey along with the development levels of provinces

• Published in: Open health 2023-12, Vol.4 (1), p.102995-85
• Main Authors: Icoz, Cenk, Ozcetin, Erdener
• Format: Article
• Language: English
• Subjects: COVID-19; development index; extremely randomized tree regression; multivariate local spatial autocorrelation; spatial analysis; Türkiye (Turkey)
• ISSN: 2544-9826; 2544-9826
• DOI: 10.1515/ohe-2023-0014

In this study, we examine the spatial analysis of coronavirus disease 2019 (COVID-19) instances in Turkey. As a result, this analysis reveals that the geographic distribution of COVID-19 instances is associated with disparities in education, socioeconomic status, and population within individual provinces. By utilizing a composite indicator of development level based on provinces, we employ multivariate local Geary and multivariate local neighbor match tests to demonstrate the association between COVID-19 instances and the demographic and socioeconomic similarities or contrasts in provinces of Turkey. In addition, we provide an extremely randomized tree regression model to show how demographic and socioeconomic disparities affect COVID-19 instances. According to this model, the average household size, the proportion of the working-age population to the nonworking-age population, and the GDP per capita are the most important variables. The study’s main finding is that the important variables that were attained were also used to create an index for the degree of development in the Turkish regions. In other words, the same variables correlate with the degree of provincial development and the distribution of COVID-19 cases.