Place-based factors affecting COVID-19 incidences in Turkey

Ronael, Mehmet; Baycan, Tüzin

doi:10.1007/s41685-022-00257-4

Cited by 4 publications

(1 citation statement)

References 47 publications

(65 reference statements)

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“…The first article by Ronael and Baycan ( 2022 ) reveals the place-based factors affecting the spread of the COVID-19 Pandemic in Turkey. While addressing a set of physical, natural, economic, demographic, and mobility indicators, Ronael and Baycan analyze which factors are affecting the spread of the pandemic in 81 provinces in Turkey.…”

Section: Spatial Implications Of Covid-19 Pandemic In Turkeymentioning

confidence: 99%

Special Feature on social, economic, and spatial impacts of COVID-19 pandemic in Turkey

Baycan

Tuysuz

2022

Asia-Pac J Reg Sci

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This Special Feature investigates the social, economic, and spatial impacts of the COVID-19 pandemic in Turkey and highlights the factors differentiating Turkey from the other countries. The articles contributing to this Special Feature are classified into three main parts. The first group of articles addresses spatial implications of the COVID-19 pandemic in Turkey with a specific focus on the place-based factors affecting the spread of the pandemic, the determinants of pandemic-induced changes in intracity mobility, and the use of social media to forecast commercial real estate figures during COVID-19. The second group of articles investigates the social and economic implications of the COVID-19 pandemic and diversely affected economic sectors in Turkey. These articles analyze the vulnerability and resilience of regions and diversely affected economic sectors with a specific focus on the housing market that displays an opposite trend to international tendencies regarding transaction volumes and private rental housing prices. The third group of articles considers the economic impact of the COVID-19 pandemic from an international perspective. These articles analyze the impact of the COVID-19 pandemic on international trade with a specific focus on exports and the fragility of the global trade structure and network in the framework of global value chains. Analyzing the impacts of COVID-19 from different perspectives, the articles in this Special Feature reveal the factors differentiating Turkey from the other countries and highlight the challenges.

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Section: Spatial Implications Of Covid-19 Pandemic In Turkeymentioning

confidence: 99%

Special Feature on social, economic, and spatial impacts of COVID-19 pandemic in Turkey

Baycan

Tuysuz

2022

Asia-Pac J Reg Sci

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

Icoz,

Ozcetin

2023

Open Health

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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.

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Explainable Artificial Intelligence Methods for Analysis of Factors Influencing Covid-19 Cases in Türki̇ye

İçöz

2024

Preprint

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COVID-19 disease emerged in Wuhan, China, in 2019 and evolved into a pandemic that negatively affected all countries worldwide. Researchers have employed methods such as machine learning and spatial machine learning methods, including spatial and multiple linear regression, geographically weighted regression and geographical random forests, to determine the importance of factors such as sociocultural, demographic, environmental, racial, and economic development related to COVID-19 cases or deaths caused by COVID-19. In this study, the explainability of the general factors analyzed by the random forest model of COVID-19 cases based on provinces in Türkiye was examined. In addition, traditional machine learning methods, compared with spatial machine learning models, as the Explainable Artificial Intelligence (XAI) methods directed toward employing spatial associations. The most important factors in the model might differ locally among provinces according to cluster. The spatial machine learning models performed better than the random forest model.

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Place-based factors affecting COVID-19 incidences in Turkey

Cited by 4 publications

References 47 publications

Special Feature on social, economic, and spatial impacts of COVID-19 pandemic in Turkey

Special Feature on social, economic, and spatial impacts of COVID-19 pandemic in Turkey

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

Explainable Artificial Intelligence Methods for Analysis of Factors Influencing Covid-19 Cases in Türki̇ye

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