**3. Findings**

#### **3.1 Spatial map of Total number of cases in Turkey**

First, we examine the spatial variation of the total number of cases in Turkey. The spread of Covid-19 cases across Turkish provinces is shown in **Figure 1**.

The map in **Figure 1** demonstrates that there are regional variations in the diffusion of Covid-19 cases. The total number of confirmed cases increases from west to east of Turkey. We also consider the four main regions of Turkey and statistically compare the average cases in these regions. These regions are defined as follows: *i*. Marmara, Aegean and Mediterranean Regions, *ii*. Black Sea Region, *iii*. Central

**Figure 1.** *The variation of Covid-19 cases across provinces in Turkey in July 2021.*

Anatolia Region, and *iv*. Eastern and Southeastern Anatolia. Since the normal distribution assumption of ANOVA cannot be satisfied, we compare the means of these regions with the aid of Kruskal–Wallis test. The results reject the equality of means of the Covid-19 cases across regions at a 5% level (χ<sup>2</sup> = 8.757, p–value = 0.0327). Pairwise comparisons revealed that Eastern and Southeastern Anatolia have statistically higher rates than all other regions in Turkey. This result also supports the visual findings in **Figure 1**.

### **3.2 Results from spatial modeling**

We begin our analysis with the classical OLS model. By excluding the insignificant variables at the 10% level, we refine the model and obtain the ultimate model. We check the OLS assumptions. We find heteroskedasticity in our main model which might be a result of spatial dependency.

As explained before, since the association between the total number of cases and the various socioeconomic variables is not well discussed in the previous literature, we start with an SDM specification to avoid the omitted variable problem [23], which is also the linear combination of SAR and SEM specifications [30]. However, the SDM specification does not show significant results. The LR tests comparing SDM vs. OLS and SAR vs. OLS cannot reject the null hypothesis of no significant global interactions. The lack of significant global spillovers indicates that the burden of the disease at one location is not affected by the burden of the disease in the neighboring locations. Yet, the LR test for the coefficients of local interactions in the SDM specification is significant at the 1% level (LR test is 52.5983, and the p–value is 0.0015). That is to say, although no global impacts can be detected in the transmission process of Covid-19 cases in Turkey, geography still matters in the form of local interactions. The socioeconomic features of neighboring provinces are influential on the spread of Covid-19 in a given province. This finding is in line with the study by [18] in which an SLX model is found appropriate to model the new cases in mainland China. Therefore, following [23], we continue our analysis with an SLX model. The final SLX model and the OLS model as a benchmark are shown in **Table 2**.

Vaccination is clearly the strongest weapon in the fight against Covid-19. The findings from **Table 2** also confirm this situation and reveal that the vaccination rate and the total number of cases are significantly and negatively related. Interestingly, it


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

#### **Table 2.**

*The impact of socioeconomic variables on the total cases of Covid-19: OLS and SLX models.*

is found that the effect of vaccination rates in the neighboring provinces is positive and significant. That is, the increased rates of vaccination in the neighboring locations cause a growth in the total number of cases in a given province. This result seems puzzling at first, but it can be explained by the vaccine hesitancy concept. Vaccine hesitancy is defined as the "delay in acceptance or refusal of vaccination despite the availability of vaccination services" [31]. Ke and Zhou [32] state that the vaccine uptake decision of an individual can be dependent on the actions of the neighbors. They call this concept *"neighbor–reliant immunity*". They argue that people that are hesitant toward the Covid-19 vaccine might feel more "immune" without uptaking the vaccine itself if the people around are already vaccinated. This situation is visible here as well. It is seen that people with Covid-19 vaccine hesitancy do not limit their actions as much as before with neighboring provinces as the vaccination rate of neighboring provinces increases. As a result, the number of confirmed cases in a given province increases.

We also show that as the satisfaction rate with health status increases, the number of total cases also rises in a particular province. This finding can be attributed to the fact that Covid-19 is mostly perceived as an older people's disease or only dangerous for people with co–morbidities. To fight this perception, World Health Organization (WHO) made many announcements, including the one that the Chief of WHO explained that "*young people are not invincible*". It seems that this perception is still valid in July 2021 in Turkey, and it might grow even stronger with the relatively less severe variants and the ongoing vaccination process.

The rate of membership to political parties is an indicator of civic engagement. As this variable has a higher rate, the social relations, and connections increase as well. This would make it difficult to keep the social distance and adapt to "stay at home" calls. Our findings in **Table 2** confirm this result and demonstrate a positive effect of this variable on the total number of cases.

Median age, itself, is not a determinant of the spread of Covid-19 cases across Turkish provinces. However, the median age of the neighbors negatively impacts the number of cases in a given province. This finding is in line with [15]. He notes that the increase in the median age of neighbors reduces the social interactions with the given state and traveling, so less spread has occurred.

The satisfaction rate with social relations is a proxy for social life. Our results indicate that the higher values of this variable are related to a lower level of total cases. It seems that people who are more satisfied with their social life are most likely to keep their social distance and less engaged with many people. This finding might be explained by the existence of video–calls and other telecommunication methods. Individuals may meet their social needs via the internet and stay at home at the same time.

We cannot show any significant effect of housing conditions, work life, income and wealth, or health indicators other than health status, education, safety, and access to infrastructure services, however.

The results of this paper once more emphasize the importance of vaccinations in order to control the number of cases. In the case of such infectious diseases, governments must use clear communication channels with society to avoid misperceptions about the nature of the disease or the precautions to avoid further spread. Our findings show that over–confidence about the individual health status and vaccine hesitancy increase the number of total cases, so the burden on the health care system.
