| Yazarlar |
Figen Büyükakın
Türkiye |
|
Ayfer Özyılmaz
Türkiye |
|
Esme Işık
Malatya Turgut Özal Üniversitesi, Türkiye |
|
Yüksel Bayraktar
Ankara Üniversitesi, Türkiye |
Öğr. Gör. Mehmet Fırat OLGUN
Kastamonu Üniversitesi, Türkiye |
| Özet |
| Refugees are more vulnerable to COVID-19 due to factors such as low standard of living, accommodation in crowded households, difficulty in receiving health care due to high treatment costs in some countries, and inability to access public health and social services. The increasing income inequalities, anxiety about providing minimum living conditions, and fear of being unemployed compel refugees to continue their jobs, and this affects the number of cases and case-related deaths. The aim of the study is to analyze the impact of refugees and income inequality on COVID-19 cases and deaths in 95 countries for the year 2021 using Poisson regression, Negative Binomial Regression, and Machine Learning methods. According to the estimation results, refugees and income inequalities increase both COVID-19 cases and deaths. On the other hand, the impact of income inequality on COVID-19 cases and deaths is stronger than on refugees. |
| Anahtar Kelimeler |
| COVID-19,income inequality,refugees,machine learning,negative binomial regression |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | Social Work in Public Health |
| Dergi ISSN | 1937-1918 |
| Dergi Tarandığı Indeksler | SSCI |
| Dergi Grubu | Q1 |
| Makale Dili | Türkçe |
| Basım Tarihi | 01-2024 |
| Cilt No | 39 |
| Sayı | 1 |
| Sayfalar | 78 / 92 |
| Doi Numarası | 10.1080/19371918.2024.2318372 |
| Makale Linki | http://dx.doi.org/10.1080/19371918.2024.2318372 |
| Atıf Sayıları |
