**1. Introduction**

Coronavirus Disease 19 (Covid-19) marked the years 2020 and 2021 with its very fast diffusion rates and severity. With the quick development of vaccines against the disease, the pandemic right now seems to come to an end. Yet, living the last 2 years with a contagious disease has left some serious questions: What is the role of socio– economic determinants in the transmission of an airborne contagious disease like Covid–19? What factors are most influential and make countries more vulnerable to such diseases? What is the role of spatiality in the spread? In this study, we aim to investigate the answers to these questions for Turkey. More specifically, we try to point out the most influential socio–economic factors in the spread of Covid-19 in Turkey in a spatial setting.

The first Covid-19 case is confirmed in Turkey on 11th March 2020 in İstanbul. It spread quickly all over the country. To limit its transmission among the Turkish provinces, similar strategies to other countries, such as travel restrictions and partial curfews, were applied in the initial days. Yet, in time, it has become clear that every country has its own dynamics that limit the effectiveness of precautions against the Covid-19. For example, [1] find that the extreme poverty level is an important determinant in the national performance of low– and middle–income countries, since it determines the ability of social distancing. They also note that the disadvantaged share of the population in terms of socio–economic status is more vulnerable to contagious diseases. Therefore, each country must be assessed individually to understand its needs and to be prepared for future diseases. Analyzing the spread of the Covid-19 and the socio–economic determinants behind is important to be ready for any country as well as Turkey.

The ties between the socio–economic status in the spread of Covid-19 were discussed previously in the literature. These studies mainly focus on mainland China [2, 3] and the USA [4, 5]. Some of them compare the national performances of many countries based on the socio–economic variables, (e.g., [6–8]). Yet, as [4] clearly state, "*In a quickly changing pandemic landscape...county-level data and analysis is crucial to understanding needs and supporting planning efforts."* We, therefore, turn our attention to Turkey, which is one of the most affected countries in the world. Jain and Singh [9] indicate that 60% of the cases in Asia clustered in Turkey alongside mainland China and Iran. Yet, the number of studies examining the impacts of these variables on the spread of Covid-19 is still limited (among these studies, one can note the study by [10]. Our paper aims to fill this gap while considering the effects of being close to the places where the Covid-19 cases are dense.

Ref. [11] emphasize the role of spatiality in the analysis of contagious diseases by stating that "*when people move, they take contagious diseases with them*.". Much before the Covid-19 pandemic, [12] indicates that infectious diseases are the main concerns of medical geography which defines the "place" as a vital dimension of the transmission process besides the other risk factors. In the SARS epidemic example, [13] notes the importance of detecting spatial linkages which shows the potential spreading ways and spatial clusters. Similarly, [14] argues that the diffusion of infectious diseases is directly related to the location. As a result, to understand the diffusion process of such diseases, spatial analysis is a requirement.

Although the importance of location in the transmission process of such diseases besides the other risk factors is mentioned heavily in the literature, studies considering geography in the Covid-19 incidence rates are scarce and they mostly make a choice between the spatial autoregressive model (SAR) and spatial error model (SEM). Ehlert [15], for example, attempts to determine the socio-economic and region-specific in the Covid-19 transmission in German counties with a choice between SAR and SEM specifications. Andersen et al. [16] examine the local transmission of Covid-19 cases in the USA. Again, they made a selection between SAR and SEM based on the Lagrange Multiplier (LM) tests. Sun et al. [17] employ SAR, SEM, and SAC models to detect the Covid-19 period prevalence in the US counties. Baum and Henry [4] consider several demographic factors and income as well as air pollution and health-related variables in order to explain the spread of Covid-19 in the US states. They also employ a SAR model. Guliyev [18] use the number of confirmed new cases in mainland China as the dependent variable where the recovered cases and the rate of deaths are the explanatory variables in a spatial panel setting. He compares SEM and SAR models, but cannot show spatiality in the explanation of the rate of new

#### *The Socio-Economic Factors of the Covid-19 Pandemic in Turkey: A Spatial Perspective DOI: http://dx.doi.org/10.5772/intechopen.106048*

cases. He concludes that the spatial lag of X (SLX) model fits the nature of local spillovers in this association for China.

The situation for the scarce studies that consider the spread of Covid-19 in Turkey from a spatial perspective is parallel to the world literature. Tuzcu [8] provides an exploratory spatial analysis with different weight matrices for Turkish Covid-19 cases and deaths in which high spatial autocorrelation is detected particularly for major Turkish provinces. Similarly, [19] use Moran I and Local Indicator Spatial Association (LISA) statistics to determine the hot and cold spots among Turkish provinces. Dinç and Erilli [20] examine the effects of a group of socio–economic determinants as well as climate–based variables on the number of Covid-19 cases with SEM and SAR specifications. Göktaş [21] looks at the relationship between centrality in terms of trade, transportation, and health and the number of cases in a Turkish province while considering other socioeconomic factors as control variables. For this aim, he employs SAR and SAC models. Aral and Bakır [22] use the impact of population density, elderly dependency ratio, Gross Domestic Product (GDP) per capita, literacy rate, and health capacity variables to explain the diffusion of Covid-19 in Turkey with a SAR model. They find global spillovers and significant coefficients for population density and elderly dependency ratio while explaining the increase in the Covid-19 cases.

With this study, we also contribute to the scarce literature on Covid-19 studies in Turkey with a spatial perspective. One of the novelties of this paper comes from the spatial model it adopts. Unlike the previous spatial studies on Covid-19 diffusion, we argue that a spatial Durbin model (SDM) must be the first model to adopt for the analysis. The SDM approach is well known for containing both the global and local spillovers at the same time, which is a feature of the Covid-19 pandemic. In fact, when the best describing model is unknown, [23] suggests using SDM as a starting point as well. As a result, we start our analysis with an SDM setting to detect the local and global spillovers in the diffusion of Covid-19 cases across 81 Turkish provinces. Different from the existing studies, we use the vaccination rates and sub–indicators of Life Index in Provinces by the Turkish Statistical Institute (TSI) as the explanatory variables. Life Index in Provinces report includes 41 sub–indicators about income, work life, safety, housing, environment, social life, access to infrastructure services, education, life satisfaction, and civic engagement. By using these sub–indicators, we believe that every aspect of socioeconomic status in Turkish provinces, from per km2 green area to health capacity, can be taken into account. Hence, an exhaustive list of variables that have the potential to impact the spread of Covid-19 is considered. Controlling the vaccination rates also allows us to detect its role among other variables and its impact on the spread of the disease. By doing so, we are able to contribute to the very limited literature on Covid-19 vaccine hesitancy.

To the extent of our knowledge, a similar study to our setting that examines the spread of Covid-19 in Turkey belongs to [24]. He employs 11 leading indicators of the Life Index in Provinces report, not the sub–indicators as well as other socioeconomic and environmental variables such as GDP, household size, age, air quality, humidity, and average temperature. Although this study also mentions the spatial distribution of Covid-19 cases in Turkey and benefits from some spatial maps, the main analysis method is Ordinary Least–Squares (OLS), not spatial models. By using spatial analysis methods with an exhaustive set of socioeconomic indicators, we believe that our study closes an important gap in the literature.

Our results indicate no significant global impacts in the spread of Covid-19 cases across Turkey, but significant local interactions. We show that vaccination in a given province decreases the total number of cases per hundred thousand people in the same province, but increases the Covid-19 cases in the neighboring province. This seemingly puzzling finding is a result of vaccine hesitancy toward Covid-19 vaccines. The "*neighbor–reliant immunity*" argument by [1] explains that people with vaccine hesitancy feel safer when more people around are vaccinated, so they can act more freely. This situation significantly and negatively affects the total number of cases. We also find that people that are more satisfied with their health status act more carelessly, and the number of total cases increases significantly with higher levels of this variable. The median age of neighbors and the satisfaction rate with a social life are variables that are inversely related to the number of total cases. As the median age of neighbors increases, the social interactions and traveling between provinces decreases to avoid the negative consequences of Covid-19. On the contrary, the rate of membership to political parties in a given province is positively related to the total number of cases in the same province. This finding can be attributed to more social interactions and less social distancing with increased civic engagement.

Based on the findings of this study, we can suggest that the usage of clear communication channels with society has vital importance in fighting against infectious diseases. In this way, it is possible to correct the misperceptions both about the nature of the disease and the vaccinations. Overconfidence about the health status and vaccine hesitancy might increase the overall number of cases, so the burden on the health care system.

The rest of the study continues with an explanation of the data and methodology utilized. The next section presents our findings. The last section concludes with the policy suggestions to the Turkish authorities for the next pandemics.
