Did Human Microbes Affect Tourist Arrivals before the COVID-19 Shock? Pre-Effect Forecasting Model for Slovenia
Abstract
:1. Introduction
2. Overview of Some Shocks to Public Health and Tourist Arrivals
2.1. Modelling and Literature Overview
- Post-effect research (analysing already known events);
- Pre-effect research (detecting and discovering prophecy).
2.2. Empirical Phenomena of Pandemic Pattern and Public Health
3. Methodology
- One hundred thirty-two (132) monthly observations from January 2008 to December 2018. This was an initial pre-effect period and the main goal of disseminating the research. This part is presented in the next section, the Results section;
- One hundred fifty-six (156) monthly observations from January 2008 to December 2020. This is post-effect research and a subsequent goal of the investigation, which is presented in Appendix B to gain the robustness of the study. On top of that, the dependent variable was split into domestic tourist arrivals and foreign tourist arrivals. Foreign tourist arrival was tested separately for the country of origin, where the three most crucial outbound countries were used, e.g., Germany, Austria, and The Netherlands.
4. Results
- Second, the decline was predicted on a pre-effect basis in 2019 [2];
- Third, two non-conventional variables were treated in reliable modelling;
- Fourth, the ARIMA model was used to forecast the event;
- Fifth, a dummy variable for seasonal patterns of virus spread is added;
- Finally, the tourism boom could be based on bacteria (E. coli, Campylobacter) in 2022/2023.
5. Delimitations and Limitations
5.1. Delimitations
5.2. Implications
5.3. Limitations and Discussion
6. Conclusions
6.1. Theoretical Implications
6.2. Managerial Implications
6.3. Future Research
6.4. Conclusions in Short
- The first examined period (2008–2018) forecasted a decline in tourist arrivals due to viruses. Each infected person in the destination country (e.g., Slovenia) affects a decline in 41.81 tourists. On the other hand, E. coli and Campylobacter have opposite consequences on tourist arrivals. Each person infected by E. coli in the destination country (e.g., Slovenia) modified tourist arrivals by a rise of 3807 persons and by Campylobacter a rise of 21,061 travelers per month;
- The second studied period (2008–2020) confirms the robustness of the predicted results. This is presented in several ways. First, the overlayed images (Figure 3) show the expected and actual outcomes. Second, the wave in tourist arrivals in 2019/2020 using the ARIMA and VECM modelling procedure was exactly predicted, which validates the choice of variables and methods. The decline in 2020 was 75.43%. Finally, the expanded and split analysis is presented in Appendix B. These results confirm the forecasted wavering in tourist arrivals.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Author(s) | Main Finding | Methodology |
---|---|---|
[57] | The main goal of this case study is to analyse the air traffic, air cargo, and the safety-hygiene air corridor between the United Kingdom (UK) and Spain. Air traffic that airlines project onto the UK-Spain corridor has decreased due to the pandemic. In the medium term, implementing the new Safety-Hygiene Air Corridor (SHAC) will return to nurture airlines, airports and destinations economically. | A case study |
[58] | The drastic drop in flight frequencies at airports during the pandemic has caused an average decrease of 65% in passenger arrivals until October 2020, which is 23 million passengers; too many passengers for the Andalusian economy, which depends mainly on the tourism sector. | A review of the relevant literature, secondary data research |
[59] | An instrument to measure the influence of coronavirus (COVID-19) on international travellers’ behaviour has been developed. Five hundred respondents in the Kingdom of Saudi Arabia were surveyed to create and validate a scale to measure international travel behaviour post-COVID-19. Findings revealed a hierarchical three-level scale for measuring international travellers’ behaviour. The tourism and hospitality industry can use the scale to assess the impact of COVID-19 or any future pandemic. | A questionnaire, Factor analysis |
[60] | The COVID-19 virus was a cataclysmic event that will change the course of human history in numerous ways. At the time that this editorial was written, there were 113 million cases of infections worldwide, which resulted in 2.5 million deaths. | Expose |
[62] | Econometric testing is conducted on a sample of 205 countries/territories. The results show a positive and significant relationship between COVID-19 and tourist arrivals per capita. This finding suggests that the tourism specialisation model in the small island context is too vulnerable to be considered sustainable in the medium to long term. | Time series |
[63] | The monthly foodborne disease incidence in Shenzhen from January 2012 to December 2017 was between 954 and 32,863. The mean absolute percentage error (MAPE) was used to assess the model’s performance. The ARIMA (1,1,0) model was adequate for the change in month-to-month data. | ARIMA |
[64] | Since 1980, the world has been threatened by different waves of emerging disease epidemics. It is difficult to stop the occurrence of new pathogens in the future due to the interconnection among humans, animals, and the environment. However, it is possible to face a new disease or reduce its spread risk. | Expose of time series |
[65] | The paper provides a short-run estimation of international tourism demand focusing on the case of the FYR of Macedonia. The forecasted values of the chosen model can assist in mitigating any potential negative impacts on the country’s tourism development plan. | ARIMA |
Appendix B
Country | Results |
---|---|
Domestic tourists | Dummy variable, viruses of COVID-19, and Campylobacter are negatively significant. |
Foreign tourists | A dummy variable (negatively) and E. coli (positively) are significant. |
All inbound tourists | A dummy variable (negatively) and E. coli (positively) are significant. |
Country-specific tourists (Germany) | A dummy variable and viruses of COVID-19 (negatively) and E. coli (positively) are significant. |
Country-specific tourists (Netherlands) | A dummy variable (negatively) and E. coli (positively) are significant. |
Country-specific tourists (Austria) | A dummy variable and viruses of COVID-19 are negatively significant. |
Appendix C
Appendix D
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Gričar, S.; Bojnec, Š. Did Human Microbes Affect Tourist Arrivals before the COVID-19 Shock? Pre-Effect Forecasting Model for Slovenia. Int. J. Environ. Res. Public Health 2022, 19, 13482. https://doi.org/10.3390/ijerph192013482
Gričar S, Bojnec Š. Did Human Microbes Affect Tourist Arrivals before the COVID-19 Shock? Pre-Effect Forecasting Model for Slovenia. International Journal of Environmental Research and Public Health. 2022; 19(20):13482. https://doi.org/10.3390/ijerph192013482
Chicago/Turabian StyleGričar, Sergej, and Štefan Bojnec. 2022. "Did Human Microbes Affect Tourist Arrivals before the COVID-19 Shock? Pre-Effect Forecasting Model for Slovenia" International Journal of Environmental Research and Public Health 19, no. 20: 13482. https://doi.org/10.3390/ijerph192013482