Objective: In this study, the effects of social and health indicators affecting the number of cases and deaths of the COVID-19 pandemic were examined. For the determinants of the number of cases and deaths, four models consisting of social and health indicators were created. Methods: In this quantitative research, 93 countries in the model were used to obtain determinants of the confirmed cases and determinants of the COVID-19 fatalities. Results: The results obtained from Model I, in which the number of cases was examined with social indicators, showed that the number of tourists, the population between the ages of 15 and 64, and institutionalization had a positive effect on the number of cases. The results obtained from the health indicators of the number of cases show that cigarette consumption affects the number of cases positively in the 50th quantile, the death rate under the age of five affects the number of cases negatively in all quantiles, and vaccination positively affects the number of cases in 25th and 75th quantile values. Findings from social indicators of the number of COVID-19 deaths show that life expectancy negatively affects the number of deaths in the 25th and 50th quantiles. The population over the age of 65 and CO2 positively affect the number of deaths at the 25th, 50th, and 75th quantiles. There is a non-linear relationship between the number of cases and the number of deaths at the 50th and 75th quantile values. An increase in the number of cases increases the number of deaths to the turning point; after the turning point, an increase in the number of cases decreases the death rate. Herd immunity has an important role in obtaining this finding. As a health indicator, it was seen that the number of cases positively affected the number of deaths in the 50th and 75th quantile values and the vaccination rate in the 25th and 75th quantile values. Diabetes affects the number of deaths positively in the 75th quantile. Conclusion: The population aged 15–64 has a strong impact on COVID-19 cases, but in COVID-19 deaths, life expectancy is a strong variable. On the other hand, it has been found that vaccination and the number of cases interaction term has an effect on the mortality rate. The number of cases has a non-linear effect on the number of deaths.
The aim of this study is to investigate the effect of health expenditures on economic growth in the period 2000–2019 in 27 European Union (EU) countries. First, the causality relationship between the variables was analyzed using the panel Fourier Toda–Yamamoto Causality test. The findings demonstrate a bidirectional causality relationship between health expenditures and economic growth on a panel basis. Secondly, the effects of health expenditures on economic growth were examined using the Random Forest Method for the panel and then for each country. According to the Random Forest Method, health expenditures positively affected economic growth, but on the country basis, the effect was different. Then, government health expenditures, private health expenditures, and out-of-pocket expenditures were used, and these three variables were ranked in order of importance in terms of their effects on growth using the Random Forest Method. Accordingly, government health expenditures were the most important variable for economic growth. Finally, Support Vector Regression, Gaussian Process Regression, and Decision Tree Regression models were designed for the simulation of the data used in this study, and the performances of the designed models were analyzed.
Alzheimer’s disease will affect more people with increases in the elderly population, as the elderly population of countries everywhere generally rises significantly. However, other factors such as regional climates, environmental conditions and even eating and drinking habits may trigger Alzheimer’s disease or affect the life quality of individuals already suffering from this disease. Today, the subject of biomedical engineering is being studied intensively by many researchers considering that it has the potential to produce solutions to various diseases such as Alzheimer’s caused by problems in molecule or cell communication. In this study, firstly, a molecular communication model with the potential to be used in the treatment and/or diagnosis of Alzheimer’s disease was proposed, and its results were analyzed with an artificial neural network model. Secondly, the ratio of people suffering from Alzheimer’s disease to the total population, along with data of educational status, income inequality, poverty threshold, and the number of the poor in Turkey were subjected to detailed distribution analysis by using the random forest model statistically. As a result of the study, it was determined that a higher income level was causally associated with a lower risk of Alzheimer’s disease.
ÖzetTarımsal destekleme ödemeleri, tarım sektörünü korumak için uygulanan önemli bir politikadır. Destekleme ödemelerinin GSYH'ya oranı açısından Türkiye, OECD ve AB ülkeleri arasında en yüksek tarımsal desteğe sahip ülkedir. Bu durum, büyük ölçüde Türk tarım sektöründeki yapısal sorunlardan kaynaklanmaktadır. Bu çerçevede çalışmanın konusunu, Türkiye'deki tarımsal desteklerde meydana gelen değişim ve yüksek tarımsal desteklerin nedenleri oluşturmaktadır. Bunun için büyük ölçüde OECD, AB ve Türkiye'ye ait veriler ele alınmış ve karşılaştırmalı olarak analiz edilmiştir. Çalışma sonucunda; tarımsal istidamın, tarımsal faaliyetlerden elde edilen üretici gelir düzeyinin, destekleme niteliğinin ve ürün bazlı verilen destekleme politikalarının, Türkiye'de uygulanmakta olan yüksek tarımsal desteklerin esas sebepleri olduğu gösterilmiştir. AbstractAgricultural support payment is an important policy for the protection of agricultural sector. In terms of the ratio of agricultural support payments to GDP, Turkey is the country that has the highest agricultural supports among the OECD and the EU's countries. This situation largely arises from the structural problems of the Turkish agricultural sector. In this framework, the reasons, which cause high agricultural support in Turkey, establish the subject of this study. Therefore, the data that belong to the OECD, EU and Turkey were taken into account and analyzed comparatively. At the end of the work it was revealed that, the main reasons of high agricultural supports which are implemented in Turkey are; agricultural employment, manufacturer's level of income derived from agricultural activities, the feature of support and product-based support policies.
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