Outbound, Inbound and Domestic Tourism in the Post-COVID-19 Era in OECD Countries

Abstract

The relationship between COVID-19 and the tourism industry has important lessons for the post-pandemic period. The tourism industry is undergoing major changes after the pandemic. Analyzing the impact of tourism on the spread of coronavirus around the world may help us to understand how it could be a catalyst for spreading epidemics. To investigate the impact of the tourism industry on the spread of coronavirus, tourism data, as well as cases of coronavirus in the year 2020–2021, were used for OECD countries. The quantile regression method was used to estimate the effects of different types of tourism on the spread of coronavirus. The results showed that, in the first season of 2020, all types of tourists had an impact on the spread of the coronavirus. However, until the end of 2020, only outbound tourism had a significant impact on total deaths caused by the coronavirus, and in 2021, the tourism industry did not have any significant effect on the total deaths caused by the coronavirus. The findings of this article show that prior preparedness, comprehensive guidelines and roadmaps, and the establishment of international travel monitoring agencies are required to assess global constraints in critical situations. Advanced systems for controlling domestic travel in a country at a time of contagious diseases are essential.

1. Introduction

The current COVID-19 pandemic remains one of the greatest outside shocks leading to a worldwide crisis that has affected the global tourism industry [1]. The tourism sector has become an integral part of economic growth strategies and determinants over the years [2]. The growth of the travel industry has had many positive consequences and has led to economic growth in many countries. The economic growth of some countries relies heavily on the growth of the travel industry. In addition, the growth of the travel industry has had other positive consequences, such as job creation, exchange of cultures and lifestyles, and information transfer. In that context, with globalization, travelers all over the world could travel to other countries in a few hours [3].

However, the increase in tourism, the expansion of travel, and the close interaction of people can also have negative consequences. One of the most important consequences of the expansion of the tourism industry has been the increasing rate of the transmission of communicable diseases in recent years. During the 2003 SARS pandemic, researchers showed that SARS and tourism are interlinked. tourists belonging to affected areas at the early stages of the outbreak became vectors of the disorder, which eventually caused travel and tourism to become victims of the epidemic [4].

The relationship between domestic train transportation and the novel coronavirus showed a strong and significant association between travel by train and the number of COVID-19 cases, which increased 10-fold regarding the number of train passengers from Wuhan [5]. Another study showed a significant and positive association between the frequency of daily flights, trains, and buses from Wuhan and the cumulative number of COVID-19 cases in other cities, with progressively increasing correlations between trains and buses. Hence, cases imported via public transportation played an important role in the spread of coronavirus [6].

Intercontinental travel contributed to the spread of extended-spectrum beta-lactamase-producing enterobacterales (ESBL-PE) [7]. Incoming travelers also played an important role in the entry of coronavirus to Romania [8]. Another study showed the importance of the role of immigration and travel in the epidemiology of mumps and rubella in the Kingdom of Saudi Arabia [9].

The bizarre pneumonia cases caused by a novel coronavirus (2019-nCoV) were first diagnosed and noted in Wuhan, China, in December 2019 [5]. However, patients had introduced the virus to different cities during the incubation period, and the person-to-person transmission of the new coronavirus spread the infection across the country [6]. Another study warned of the possible spread of the coronavirus via commercial air travel from Wuhan, China, to other countries [10].

The COVID-19 pandemic is the third serious coronavirus outbreak in less than 20 years, following SARS in 2002–2003 and MERS in 2012 [11]. Two decades ago, researchers believed that if SARS reoccurred, the subsequent outbreak could be smaller and more easily contained if the lessons learned from that episode were applied [4]. However, this coronavirus outbreak suggests that further research into contagious diseases and travel is needed. Given the importance of this topic, this paper examines the impact of the tourism industry and travels on the worldwide spread of coronavirus.

This paper includes six sections. Section 2 examines the lecture review and presents the research hypotheses. In Section 3, the materials and methods used in this paper are described. Section 4 will show the experimental results and statistical tables. In Section 5, the findings will be discussed. Section 6 will present the conclusions and implications.

2. Literature Review and Hypothesis Development

The world health organization (WHO) reported the first case of coronavirus in Wuhan, China, in December 2019. As of 18 February 2020, the virus had caused over 2200 deaths, and global cases of COVID-19 infection exceeded 75,740 [12,13]. However, with the outbreak of the COVID-19 pandemic in March 2020, measures were introduced by countrywide governments, which included border closures, journey bans and quarantine policies [14]. The tourism enterprise has overcome many challenges to date, some of them with worldwide effects, such as the 11th of September attacks, the monetary disaster of 2008–2010, and the intense acute respiration syndrome (SARS) and Middle East respiratory syndrome (MERS) outbreaks [15,16]. Tourism is the branch of the economy that was most laid low by the COVID-19 pandemic, with long-term effects [17]. The COVID-19 pandemic was verified to be one of the most important challenges according the travel and tourism sector records of the worldwide financial system [18,19]. Countries took action to restrict the spread of the coronavirus, such as general or partial lockdowns and strict restrictions on gatherings of human beings in public [20].

The pandemic affected the arrival of travelers, which declined by seventy four percent, approximately 1 billion trips, within January–December 2020 [21,22]. In 2018, Europe ranked first globally in global arrivals, with 713 million, over 1/2 of the worldwide quantity, showing an annual increase of 6% [23,24]. Before the epidemic, the tourism industry accounted for 10.6% of all jobs and 10.4% of global GDP [23,25].

The pandemic has had many effects on various industries. For example, some researchers studied rental market crisis management during the COVID-19 pandemic [26,27]. The COVID-19 pandemic has wreaked havoc on the tourism industry like never before, resulting in massive losses of revenue and jobs around the world [18]. The tourism industry was impacted by the COVID-19 pandemic and restrictive guidelines set by authorities. The airline industry and healthcare were seriously impacted by these regulations [28]. Numerous gastronomy-orientated companies declared bankruptcy and closed their institutions. Similarly, concerts, (mega) occasions, fairs, and meetings were cancelled [12,29].

Consistent with the latest research documents of the world travel and tourism council [30], in 2020, the tourism industry recorded a 73% reduction in global arrivals, constituting its worst 12 months. In 2021, global tourism experienced a 4% increase; however, tourism remained significantly lower than the pre-pandemic year of 2019 [31,32].

Domestic tourism is an ignored topic in the literature [33,34]. Domestic tourism, which might have been more popular in the past, as compared to the global journeys that travelers make at present, appears to be regaining its credentials in the context of the COVID-19 crisis [33]. COVID-19 has modified tourism through the distinct public health measures that were implemented [35].

A pandemic is an ailment that takes place over a huge geographic area and affects an excessive percentage of the population [36]. Traditionally, catastrophes consisting of plagues have had several financial, psychological and social impacts on human beings and places [37]. Catastrophes such as pandemics have no barriers. The increase in tourism worldwide led to concerns regarding the outbreak of infectious diseases [38] due to people’s extensive mobility [39]. The disaster at the close of 2019 caused huge losses, specifically in worldwide tourism, which was seen as one of the main factors responsible for the spread of the pandemic [40,41].

The travel industry needs progressive solutions for sustainable recovery, but there is little research on the tourism regulations necessary for sustainable and resilient recovery within the new normal [35]. Thus, the following research hypotheses are proposed:

H1. 

The international tourist arrivals of 100 countries positively affect coronavirus cases.

H2. 

The outbound tourism in OECD countries positively affects coronavirus cases.

H3. 

The inbound tourism in OECD countries positively affects coronavirus cases.

H4. 

The domestic tourism in OECD countries positively affects coronavirus cases.

H5. 

The inbound and outbound tourism in OECD countries positively affected total deaths in 2020.

H6. 

The inbound and outbound tourism in OECD countries positively affected total deaths in three quantiles in 2020.

H7. 

The inbound and outbound tourism in OECD countries positively affected total deaths in 2021.

H8. 

The inbound and outbound tourism in OECD countries positively affected total deaths in three quantiles in 2021.

3. Materials and Methods

This section is divided into two parts: the variables and data source are first defined, and the statistical method is then explained.

3.1. Variables and Data Source

Depending on the subject, the introduction of variables and data sources will be described in the tourism and coronavirus sections. Regarding the tourism industry, Figure 1 shows the year-by-year percentage of international tourist arrivals in 2020–2022.

Figure 1 shows that, at the beginning of the coronavirus epidemic, international tourism experienced a sharp decline, and after two years, international tourism is slowly returning to its previous position.

This paper will first examine the impact of the number of international tourist arrivals (data source: www.WorldBank.org accessed on 31 December 2022) on the expansion of COVID-19. The spread of COVID-19 was not limited to international tourists, and tourists traveling abroad can bring the virus home with them. International tourists often transmit the virus to destination countries. However, the prevalence of the disease in a particular country is mostly driven by domestic tourists. In this study, the effect of three types of tourism, outbound, inbound and domestic tourism, on the spread of coronaviruses within countries was examined.

Since exact data for these three types of tourists are mostly published for developed countries, 46 countries were selected (36 member states, 9 potential member states, and China). Of these 46 countries, complete data were available for 30 countries. These statistics were collected from the www.OECD.org (accessed on 31 December 2022).

Coronavirus cases: As mentioned above, 30 countries were selected for our analysis. All countries’ coronavirus data were collected from www.worldometers.info (accessed on 31 December 2022). Figure 2 shows the number of outbound, inbound, and domestic tourism in OECD countries. As shown, there is a direct relationship between the three variables and the spread of coronavirus. That is, countries with more domestic tourism usually had more outbound and inbound tourism.

In this research, the effect of the three types of tourism on the spread of coronavirus was investigated for three time periods: the first quarter of 2020, the whole of 2020, and the whole of 2021.

3.2. Statistical Methods

In this study, we used quantile regression. Quantile regression seeks to model the relationship between X and Y for different quantiles of the distribution of the dependent variable [43,44].

As shown in [45], the regression was estimated for $\tau$ (quantiles), where:

$$Quantiles=0<\tau <1$$

(1)

The standard OLS regression model is given by:

$$E(y_i\left|x_i)={\beta }_0\right.+{\beta }_1{x}_i$$

(2)

Equation (3) can be written as:

$$y_i={\beta }_1+{\beta }_2{x}_i+u_i$$

(3)

where the error is satisfied by $E(y_i\left|x_i)=0\right.$.

The quantile model $Q_q(y_i\left|x_i)\right.$ is analogous to $E(y_i\left|x_i)\right.$ in Equation (3), but does not consider the distribution function of u_i. The quantile model is written as:

$$Q_q(y_i\left|x_i)\right.={\beta }_1+{\beta }_2{x}_i+F_{u_i}^{-1}(\mathrm{q})$$

(4)

where F_ui is the distribution function of u_i and conditional or dependent on x_i [45].

We follow [43] by generalizing the optimization procedure for a certain quantile of interest, τ, as follows: [46].

$$\underset{\beta \in IR}{\min }\left[{\displaystyle {\sum }_{t\in (t:y\ge x_t\beta }\tau \left|y_t-x_t{\beta }_{\tau }\right|+{\displaystyle {\sum }_{t\in (t:y<x_t\beta }(1-\tau )\left|y_t-x_t{\beta }_{\tau }\right|}}\right]$$

(5)

where Y is the individual expenditure per match attendee. X is defined as the set of independent variables. βi are the k coefficients of the quantile regression. Τ is the weight of the positive residues and (1 − τ) is the weight of the negative residues [44].

The estimation of the parameters, within the case of quantile regression (Figure 3), is finished by minimizing weighted absolute deviations with asymmetrical weights. One of the essential advantages of using quantile regression in place of an OLS is the use of deviations in absolute values, as opposed to to the squared deviations, to estimate the parameters βi. The estimates furnished via quantile regression are notably unaltered by intense values, as it penalizes errors linearly, while OLS regression gives greater importance to increasing mistakes in the squared deviations [47]. During model validation, the strongest benefit of quantile regression becomes its strongest disadvantage [44].

4. Results

4.1. The Effects of Tourism on the Coronavirus Outbreak for 100 Countries

To examine the effect of tourism on the prevalence of coronavirus, we used the data on international tourist arrivals for 100 countries. The charts of the number of coronavirus cases and the number of tourists for 100 countries from different continents are included in Figure 4, where the countries appear to have different colors and the size of each bubble in Figure 4 shows the share of travel and tourism’s direct contribution to GDP. Figure 4 shows coronavirus cases by continent. Each continent’s share of coronavirus cases in Figure 4 is in line with the increase in tourism in that continent. Is there a relationship between the number of tourists on each continent and the reported coronavirus cases?

As can be seen from Figure 4, there is a direct relationship between the number of international tourist arrivals in each country and the coronavirus cases in those countries, but a more accurate analysis can be conducted by estimating the model. The results of this estimation are presented in

Table 1. The effect of tourism on the coronavirus outbreak for 100 countries.

Variable	Quantile	Coefficient	Std. Error	t-Statistic	Prob.
The international tourist arrivals on 14 March	33%	0.7505	0.1545	4.8590	0.0000
	50%	0.8793	0.1632	5.3887	0.0000
	66%	1.0384	0.1409	7.3698	0.0000
The international tourist arrivals on 2 April	33%	0.6535	0.1559	4.1915	0.0001
	50%	0.7470	0.1687	4.4286	0.0000
	66%	0.9000	0.1617	5.5651	0.0000

. Quantile regression was used for this estimation and the coefficients, standard deviation, t-statistics, and probability statistics for the 33%, 50% and 66% quantiles are presented in Table 1. The results of Table 1 show that, in the 33%, 50% and 66% quantiles, the coefficient of international tourist arrivals is 14 March: 0.75, 0.87, 1.03 and 2 April: 0.65, 0.74, 0.90, and this coefficient is significant in all three cases.

As shown, international tourist arrivals increase the spread of coronavirus worldwide, and with the increase in international tourist arrivals, coronavirus becomes more prevalent, with an effect in the 50% quantile.

4.2. The Effects of Outbound Tourism on the Coronavirus Outbreak in OECD Countries

For a more detailed analysis, this section examines the effect of outbound tourism on the spread of coronavirus in OECD countries. Figure 5 shows outbound tourism data on coronavirus cases in OECD countries.

Although Figure 5 intuitively shows a positive effect of outbound tourism on cases of coronavirus, the results obtained when estimating this relationship are presented in

Table 2. The effect of outbound tourism on the coronavirus outbreak in OECD countries.

Variable	Quantile	Coefficient	Std. Error	t-Statistic	Prob.
Outbound tourists on 14 March	33%	1.1307	0.3352	3.3733	0.0022
	50%	1.1015	0.3522	3.1273	0.0041
	66%	1.1923	0.3485	3.4210	0.0019
Outbound tourists on 2 April	33%	1.1447	0.2156	5.3100	0.0000
	50%	1.2031	0.2240	5.3700	0.0000
	66%	1.1526	0.1951	5.9083	0.0000

. Table 2 shows that outbound tourism’s effect on the spread of coronavirus in the 33% 50% and 66% quantiles was 14 March: 1.13, 1.10, 1.19 and 2 April: 1.14, 1.20, 1.15, respectively, which was statistically significant at the 95% confidence level in all three cases.

These results suggest that one of the reasons for the spread of coronavirus in OECD countries was the increase in outbound tourism, with this effect being in the 66% quantile on 14 March and 50% quantile on 2 April.

4.3. The Effects of Inbound Tourism on the Coronavirus Outbreak in the OECD Countries

Figure 6 shows the relationship between inbound tourism and cases of coronavirus for OECD countries. As can be seen from the graph, this is a direct relationship. The results of statistical surveys for these two variables are presented in

Table 3. The effect of inbound tourism on the coronavirus outbreak in OECD countries.

Variable	Quantile	Coefficient	Std. Error	t-Statistic	Prob.
Inbound tourists on 14 March	33%	1.2023	0.4335	2.7739	0.0097
	50%	1.2630	0.4264	2.9621	0.0062
	66%	1.2773	0.3725	3.4291	0.0019
Inbound tourists on 2 April	33%	1.3566	0.3441	3.9425	0.0005
	50%	1.4196	0.2879	4.9317	0.0000
	66%	1.3562	0.2607	5.2028	0.0000

.

The results of Table 3 show that inbound tourism had a positive effect on the cases of coronavirus in the 33% 50% and 66% quantiles, as follows: 14 March: 1.20, 1.26, 1.27 and 2 April: 1.35, 1.41, 1.35, respectively. All three coefficients were significant concerning the t-statistic. Thus, with the increase in inbound tourism, especially in the 66% quantile on 14 March and 50% quantile on 2 April, the incidence of coronavirus increases.

4.4. The Effects of Domestic Tourism on the Coronavirus Outbreak in OECD Countries

All the above analyses are related to tourists from abroad. Figure 7 shows the relationship between domestic tourists and the spread of coronavirus in the country. Figure 7 shows that this effect is direct. The results of the quantile regression are shown in

Table 4. The effect of domestic tourism on the coronavirus outbreak in OECD countries.

Variable	Quantile	Coefficient	Std. Error	t-Statistic	Prob.
Domestic tourists on 14 March	33%	0.6790	0.3084	2.2013	0.0361
	50%	0.7444	0.2875	2.5893	0.0151
	66%	0.5895	0.2434	2.4221	0.0222
Domestic tourists on 22 April	33%	0.7543	0.2240	3.3674	0.0022
	50%	0.7299	0.1910	3.8205	0.0007
	66%	0.6613	0.1611	4.1058	0.0003

.

The results of quantile regression estimation showed that the effect of the number of domestic tourists on the spread of coronavirus was positive and significant in all three quintiles (14 March: 0.67, 0.74, 0.58 and 2 April: 0.75, 0.72, 0.66), with the highest effect being found in the 50% quantile on 14 March and 33% quantile on 2 April. The results showed that all three types of tourists spread coronavirus. In the following, we will check whether inbound and outbound tourists caused the spread of the coronavirus in 2020.

4.5. The Effect of the Tourism Industry on the Total Deaths of the Coronavirus in 2020

Table 5. The correlation of four variables: total cases, total deaths, inbound and outbound tourist (2020).

Variable	Total Deaths	Total Cases	Inbound Tourism
Total Cases	0.76473
t-Statistic	5.81424
Probability	0
Inbound Tourism	0.33808	0.169245
t-Statistic	1.75991	0.841263
Probability	0.0912	0.4085
Outbound Tourism	0.59305	0.243752	0.69999
t-Statistic	3.60846	1.231272	4.80192
Probability	0.0014	0.2302	0.0001

shows the correlation of four variables: total cases, total deaths, inbound and outbound tourists. The t-statistic and probability are given in Table 5. The results in Table 5 show that the correlation of total deaths with total cases, inbound and outbound tourism is 0.764, 0.338 and 0.593, which are all significant at 90% probability. The correlation of inbound and outbound tourism with total cases is not significant. The correlation between inbound and outbound tourism is 0.69, which is significant.

After checking the correlation between the four variables of the model, we checked the effect of the three variables of total cases, inbound, and outbound tourism on total deaths in different quantiles. The behavior of these four variables in different quantiles is shown in Figure 8.

Figure 8 shows the behavior of four variables (total deaths, total cases, inbound and outbound tourism) in different quantiles. The upward trend of these four variables in different quantiles can be very informative. The results of this graph, along with the correlation coefficient in the previous table, can show the relationship between these variables. However, to investigate the effect of three variables (total cases, inbound and outbound tourism) on total deaths, quantile regression was used, and the results are shown in the

Table 6. The effect of inbound and outbound tourism on the total deaths in 2020.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
Intercept	−1607.77	4478.928	−0.358963	0.7230
Total Cases	0.012839	0.001475	8.702732	0.0000
Inbound Tourism	−0.00019	0.00017	−1.114256	0.2772
Outbound Tourism	0.002611	0.000925	2.822005	0.0099

.

The results show that the effect of total cases on total deaths was 0.0128. This effect is positive, which means that with the increase in total cases, total deaths will also increase. This effect is significant at 95% probability. The effect of inbound tourism on total deaths is negative, but according to the t-statistic −1.114, this coefficient is not significant. The effect of outbound tourism on total deaths is positive, at 0.0026, which means that the increase in outbound tourism in 2020 increased total deaths. According to t-statistic 2.822, this coefficient is significant at the level of 0.95%.

To better examine the three variables (total cases, inbound, and outbound tourism) on total deaths in different quantiles, the model was calculated in the three quantiles of 33%, 50% and 66%. The effect of these three variables in three quantiles is shown in

Table 7. The effect of inbound and outbound tourism on the total deaths in three quantiles (2020).

Variable	Quantile	Coefficient	Std. Error	t-Statistic	Prob.
Intercept	0.333	−3609.949	4933.942	−0.731656	0.4721
	0.5	−1607.77	4478.928	−0.358963	0.723
	0.667	−386.8163	4298.774	−0.089983	0.9291
Total Cases	0.333	0.013238	0.001618	8.179861	0.000
	0.5	0.012839	0.001475	8.702732	0.000
	0.667	0.012602	0.001416	8.900211	0.000
Inbound Tourism	0.333	−0.000126	0.000215	−0.588875	0.5619
	0.5	-0.00019	0.00017	−1.114256	0.2772
	0.667	−0.000227	0.000159	−1.424606	0.1683
Outbound Tourism	0.333	0.0023	0.001236	1.860952	0.0762
	0.5	0.002611	0.000925	2.822005	0.0099
	0.667	0.002793	0.00086	3.249127	0.0037

.

The effect of total cases on total deaths in 33% quantile is 0.0132, which is significant according to the t-statistic (8.179). This coefficient is 0.0128 in the 50% quantile, which is significant according to the t-statistic (8.702), and this coefficient is 0.0126 in the 66% quantile, which is significant according to the t-statistic (8.9). In general, the effect of total cases on total deaths was positive and significant in all three quantiles; that is, an increase in total cases will increase total deaths, although this effect decreases in higher quantiles.

The effect of inbound tourism on total deaths in 33% quantile is −0.000126, which is not significant according to the t-statistic (−0.588). This coefficient is obtained in 50% quantile, −0.00019; according to the t-statistic (0.277), this coefficient is not significant. This coefficient is obtained in the 66% quantile, −0.000227. According to the t-statistic (−1.42), the coefficient is not significant. In total, inbound tourism did not have a significant effect on total deaths in all three quantiles; that is, the change in the number of inbound tourisms in 2020 did not affect total deaths.

The effect of outbound tourism on total deaths in 33% quantile is 0.0023, which is not significant according to the t-statistic (1.86). This coefficient is 0.0026 in the 50% quantile, which is significant according to the t-statistic (2.822), and this coefficient is 0.0027 in the 66% quantile, which is significant according to the t-statistic (3.249). In general, the effect of outbound tourism on total deaths is positive and significant in the higher quantile; that is, the increase in outbound tourism will increase total deaths, although this effect increases in the higher quantile. Therefore, the change in the number of outbound tourisms in 2020 had a positive effect on total deaths.

4.6. The Effects of the Tourism Industry on the Total Coronavirus Deaths in 2021

After examining the effect of the three variables of total cases and inbound and outbound tourists on total deaths in 2020, this relationship was examined for 2021. The

Table 8. The correlation of four variables: total cases, total deaths, inbound and outbound tourist (2021).

Variable	Total Deaths	Total Cases	Inbound Tourism
Total Cases	0.83649
t-Statistic	7.47824
Probability	0
Inbound Tourism	0.18095	0.16227
t-Statistic	0.90135	0.80567
Probability	0.3764	0.4283
Outbound Tourism	0.48965	0.28789	0.68723
t-Statistic	2.75116	1.47275	4.63467
Probability	0.0111	0.1538	0.0001

shows the correlation between the four variables of cases, inbound and outbound tourists, and total deaths in 2021.

The results in Table 4 show that the correlation of total deaths with total cases and inbound and outbound tourism is 0.836, 0.180 and 0.489. Only the correlation between total cases and outbound tourism was significant at the level of 0.95 in terms of total deaths. The correlation of inbound and outbound tourism with total cases is not significant. The correlation between inbound and outbound tourism is 0.68, which is significant. The behavior of the four variables (total deaths, total cases, inbound and outbound tourism) is shown in Figure 9.

The results of these variables are shown in different quantiles. The results of quantile regression estimation are shown in

Table 9. The effect of inbound and outbound tourism on the total deaths in 2021.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
Intercept	−1213.662	9544.809	−0.127154	0.9000
Total Cases	0.012605	0.000986	12.78754	0.0000
Inbound Tourism	−0.00082	0.000566	−1.448915	0.1615
Outbound Tourism	0.005365	0.004147	1.293792	0.2092

.

The results show that the effect of total cases on total deaths was 0.0126. This effect is positive, which means that with the increase in total cases, total deaths will also increase in 2021. This effect is significant at 95% probability. The effect of inbound tourism on total deaths is negative, but according to the t-statistic −1.4489, this coefficient is not significant. The effect of inbound tourism on total deaths is positive, but according to the t-statistic 1.29, this coefficient is not significant.

To examine the effect of three variables (total cases, inbound, and outbound tourism) on total deaths in different quantiles, the model was calculated in the 33% 50% and 66% quantiles. The effect of these three variables in three quantiles is shown in

Table 10. The effect of inbound and outbound tourism on the total deaths in three quantiles (2021).

Variable	Quantile	Coefficient	Std. Error	t-Statistic	Prob.
Intercept	0.333	−3632.146	9426.383	−0.385317	0.7037
	0.5	−1213.662	9544.809	−0.127154	0.9000
	0.667	7117.559	9891.38	0.719572	0.4794
Total Cases	0.333	0.013241	0.000878	15.0788	0.0000
	0.5	0.012605	0.000986	12.78754	0.0000
	0.667	0.012479	0.000986	12.65214	0.0000
Inbound Tourism	0.333	−0.000478	0.000426	−1.1239	0.2732
	0.5	−0.00082	0.000566	−1.448915	0.1615
	0.667	−0.000797	0.000565	−1.412103	0.1719
Outbound Tourism	0.333	0.002919	0.002569	1.135933	0.2682
	0.5	0.005365	0.004147	1.293792	0.2092
	0.667	0.004883	0.00415	1.176671	0.2519

.

The effect of total cases on total deaths in the 33% quantile is 0.0132, which is significant according to the t-statistic (15.07). This coefficient is 0.0126 in the 50% quantile, which is significant according to the t-statistic (12.78), and this coefficient is 0.0124 in the 66% quantile, which is significant according to the t-statistic (12.65). In general, the effect of total cases on total deaths was positive and significant in all three quantiles; that is, an increase in total cases will increase total deaths, although this effect decreases in higher quantiles.

The effect of inbound tourism on total deaths in the 33% quantile is −0.000478, which is not significant according to the t-statistic (−1.1239). This coefficient is obtained in the 50% quantile, −0.00082. According to the t-statistic (−1.4489), this coefficient is not significant. This coefficient is obtained in 66% quantile, −0.000797; according to the t-statistic (−1.412), the coefficient is not significant. In general, inbound tourism did not have a significant effect on total deaths in all three quantiles; that is, the change in the number of inbound tourisms in 2021 did not affect total deaths.

The effect of outbound tourism on total deaths in the 33% quantile is 0.0029, which is not significant according to the t-statistic (1.1359). This coefficient is obtained in the 50% quantile, 0.00536; according to the t-statistic (1.29), this coefficient is not significant. This coefficient is obtained in the 66% quantile, 0.00488; according to the t-statistic (1.176), the coefficient is not significant. In general, inbound tourism has not had a significant effect on total deaths in all three quantiles; that is, the change in the number of inbound tourisms in 2021 did not affect total deaths.

5. Discussion

In December 2019, a cluster of pneumonia cases of unknown origin occurred in Wuhan, Hubei Province, China [48]. The outbreak of respiratory illness due to infection with a 2019 novel coronavirus, officially named Coronavirus Disease 2019 (COVID-19), was first noted in Wuhan, China, and spread rapidly in China and to other parts of the world [49].

One of the important issues this caused was a difference in the distribution of coronavirus in different countries (both its initial transmission and its prevalence in the country). For example, in countries with low levels of health care, such as African countries, the prevalence of contagious diseases is low, and in countries with very high levels of health care, the prevalence of contagious diseases was very high. Perhaps this contradiction can be explained by the extent of international tourism.

Many researchers showed that travel and tourism can have a great effect on the spread of infectious diseases, such as the increase in imported cases of dengue with increasing air travel seen in South Korea [50] and Italy [51], and the spread of dengue serotypes predicted by air travel seen in Brazil [52] and Asia [53,54].

Findings of international tourist arrivals for 120 countries showed a 1.33% increase in coronavirus cases, spread by a 1% increase in international tourist arrivals in the 50% quantile (Table 1). Of course, the impact of travel on the spread of infectious diseases in each country was also examined [49] for Taiwan, [55] for African countries, [56] for the Diamond Princess Cruises Ship, [57] for a group of countries, [58] and for China. Therefore, it can be said that although the increase in international tourism has positive economic and cultural effects, it can accelerate the spread of infectious diseases.

There are two types of tourists: foreign tourists who enter the country and can bring the disease with them (outbound tourism) and tourists who travel abroad and can bring the disease home (inbound tourism). The results of both groups showed that the effect of the increase in both types of tourism can lead to the increased spread of coronavirus from one country to another. The results show that a 1% increase in outbound tourism in the 66% quantile increased coronovrius spread by 1.03%, 0.90 on 14 March and 2 April, as shown in Table 2.

The results in Figure 10 show that a one percent increase in inbound tourism in the 66% quantile increased coronavirus spread by 1.27% on 14 March, and an increase in the 50% quantile increased the spread by 1.41% on 2 April, as shown in Table 3.

These two types of tourism (outbound tourism and inbound tourism) can be the initial stimulus for the transmission of coronavirus from one country to another and the spread of contagious diseases internationally. However, after the arrival of a contagious disease in any country, the outbreak is affected by domestic tourists. In other words, the prevalence of the disease in each country depends on the rate of person-to-person contact from city to city. Like SARS-CoV, 2019-nCoV can be passed directly from person to person by respiratory droplets [59] and person-to-person transmission and intercity spread of 2019-nCoV by air travel are also possible [48].

In this study, the effect of domestic tourism on the spread of coronavirus was investigated and the results showed that a one percent increase in domestic tourism in each country increased the prevalence of coronavirus by 0.7% (Table 4). The effect of tourists on the spread of coronavirus from city to city among individuals in one country has already been studied (such as [6,8,9,49,58]).

Overall, the findings of this research showed that the expansion of the tourism industry, despite all its various benefits, could play a major role in the spread of contagious diseases, as demonstrated by the spread of coronavirus.

However, these results are related to the first quarter of 2020. The results of the survey for the whole year of 2020 showed that only outbound tourism had a positive and significant effect (0.002611) on total deaths caused by the coronavirus, and inbound tourism had no effect on the total deaths caused by the coronavirus.

The model estimation results for 2021 were very surprising. In 2021, outbound tourism and inbound tourism did not have any significant impact on total deaths caused by the coronavirus. The reason for this is quite clear, because strict restrictions such as providing a PCR test for tourists were required and the number of vaccinated people increased, meaning that tourists were protected by the COVID-19 vaccine. These results suggest that a comprehensive roadmap for the tourism industry should be drawn before the next contagious disease.

6. Conclusions and Limitations

6.1. Conclusions

This article was completed in three steps. First, the impact of different types of tourism (outbound tourism, inbound tourism, domestic tourism) on the global spread of coronavirus was investigated. The results showed that all types of tourism in OECD countries increased the speed of coronavirus spread in these countries.

The results showed that, in the 33%, 50% and 66% quantiles, the coefficient of international tourist arrivals was 14 March: 0.75, 0.87, 1.03 and 2 April: 0.65, 0.74, 0.90, and this coefficient is significant in all three cases. The results showed that the effect of outbound tourism on the spread of coronavirus in the 33% 50% and 66% quantiles was 14 March: 1.13, 1.10, 1.19 and 2 April: 1.14, 1.20, 1.15) respectively, which was statistically significant at the 95% confidence level in all three cases.

The results showed that inbound tourism had a positive effect on the cases of coronavirus in the 33% 50% and 66% quantiles, as follows: 14 March: 1.20, 1.26, 1.27 and 2 April: 1.35, 1.41, 1.35), respectively. All three coefficients were significant concerning t-statistics. Thus, with the increase in inbound tourism, especially in the 66% quantile on 14 March and 50% quantile on 2 April, the incidence of coronavirus increases. The results of quantile regression estimation showed that the effect of the number of domestic tourists on the spread of coronavirus was positive and significant in all three quantiles (14 March: 0.67, 0.74, 0.58 and 2 April: 0.75, 0.72, 0.66), with the highest effect being found in the 50% quantile on 14 March and 33% quantile on 2 April.

The findings of this article do not aim to reduce international tourism and restrict the tourism industry, but rather to show that prior preparedness, comprehensive guidelines and roadmaps, the establishment of international travel monitoring agencies to assess global constraints in critical situations, and advanced systems for controlling domestic travel in any country to prevent future contagious diseases are essential, and should be considered at present.

In the second step, the effect of total cases, inbound and outbound tourism variables on total deaths caused by coronavirus in 2020 was investigated. The results showed that although the two variables of total cases and outbound tourism had a positive and significant effect on total deaths caused by the coronavirus in 2020, inbound tourism did not have any significant effect on the total deaths caused by coronavirus in 2020.

This is due to the implementation of strict restrictions on the international tourism industry. After the spread of the coronavirus in the first quarter of 2020, governments imposed strict restrictions to protect the people of their country. A PCR test was required for tourists. Suspected cases of coronavirus were quarantined. Quarantine rules became strict. Transportation restrictions became serious.

Transportation by bus, train and plane was reviewed. People suspected of coronavirus were identified and controlled. People who traveled were forced to undergo a period of quarantine. Wearing masks became mandatory for travelers and tourists. Tourist attractions, especially restaurants and hotels, had to be equipped with necessary facilities such as air conditioning. All these reduced the effect of the tourism industry on the spread of the coronavirus. These policies will help the tourism industry more in 2021.

The results of the third step showed that none of the two variables of inbound and outbound tourism have had a significant impact on total deaths caused by the coronavirus in 2021. The results were amazing. The reason for these results was the experiences of 2020, and the protection of the people of most countries by the COVID-19 vaccine. The COVID-19 vaccine greatly helped to reduce the effect the tourist industry had on total deaths caused by the coronavirus in 2021.

6.2. Limitations

The limitations of this study are as follows: this research investigated the relationship between outbound, inbound and domestic tourism in the post-COVID-19 era in OECD countries. We did not have access to data from many countries, such as developing countries. Furthermore, this research did not study the entire period of COVID-19; we did not have access to longer-term data. There were no more detailed data for the provinces of the countries for a more detailed analysis. Employment and unemployment data for the tourism industry in many countries were not available before and after the pandemic, so the relationship between COVID-19 and the tourism industry could be investigated in more detail.