Trends in Tourism Development in European Union Countries Before and After the COVID-19 Pandemic. How Quickly Has the Global Tourism Industry Recovered?

Authors

DOI:

https://doi.org/10.15611/eada.2025.4.01

Keywords:

tourism intensity indicators, the COVID-19 pandemic, EU countries trend models, adaptive models

Abstract

Aim: The purpose of the article was to determine the development trends of tourism before and after the COVID-19 pandemic and to try to answer the question: How quickly did the tourism industry recover in EU countries and return to its pre-pandemic state?

Methodology: Two research approaches were used to build forecasts of selected tourism intensity indicators. The first one uses the trend models with seasonal fluctuations estimated based on monthly data before the pandemic. The forecasts were revised considering the current situation in the tourism market in individual EU countries. Various dynamics measures were used to estimate the value of the adjustment factor. As part of the second research approach, the Holt-Winters adaptive models were used.

Results: The analysis showed that EU countries have coped with the pandemic to varying degrees. Some of them, such as those in Southern Europe, recovered to pre-pandemic levels of tourism market activity relatively quickly. Surprisingly, this did not apply equally to all countries in the region (e.g. Italy).

Implications and recommendations: The research results described can be helpful for researchers and practitioners, such as government agencies and private companies, to review the forecasts and their application in forecasting tourism demand.

Originality/value: The added value of the work was the original research approach used by the authors, combining various statistical and econometric methods to predict the direction and rate of the recovery of the global tourism industry.

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References

Abbas, J., Mubeen, R., Terhemba Iorember, P., Raza, S., & Mamirkulova, G. (2021). Exploring the impact of COVID-19 on tourism: Transformational potential and implications for a sustainable recovery of the travel and leisure industry. Current Research in Behavioral Sciences, 2, 100033.

Abbott, A. (2021). COVID's mental-health toll: How scientists are tracking a surge in depression. Nature, 590(7845), 194-195.

Akhtar, N., Khan, N., Mahroof Khan, M., Ashraf, S., Hashmi, M. S., Khan, M. M., & Hishan, S. S. (2021). Post-COVID 19 tourism: Will digital tourism replace mass tourism? Sustainability, 13(10), 5352.

Amaro, S., Barroco, C., & Fonseca, P. (2021). The use of information and communication technologies in religious tourism. The Routledge Handbook of Religious and Spiritual Tourism. Routledge.

Anderson, R. M., Heesterbeek, H., Klinkenberg, D., & Hollingsworth, T. D. (2020). How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet, 395(10228), 931-934.

Avery, E. (2017). Public information officers’ social media monitoring during the Zika virus crisis, a global health threat surrounded by public uncertainty. Public Relations Review, 43(3), 468-476.

Bąk, I., Barej, E., & Sulikowski, P. (2022). Impact of Information and Communication Technologies on the Tourism Sector. European Research Studies Journal, XXV(3), 595-606.

Campos-Soria, J. A., Inchausti-Sintes, F., & Eugenio-Martin, J. L. (2015). Understanding tourists' economizing strategies during the global economic crisis. Tourism Management, 48, 164-173.

Chang, D., & Wu, W. D. (2021). Impact of the COVID-19 Pandemic on the Tourism Industry: Applying TRIZ and DEMATEL to Construct a Decision-Making Model. Sustainability, 13(14), 7610.

Dobrovič, J., Rajnoha, R., & Šuleř, P. (2021). Tax evasion in the EU countries following a predictive analysis and a forecast model for Slovakia. Oeconomia Copernicana, 12(3), 701-728.

Ghalehkhondabi, I., Ardjmand, E., Young, W. A., & Weckman, G. R. (2019). A review of demand forecasting models and methodological developments within the tourism and passenger transportation industry. Journal Of Tourism Futures, 5(1), 75-93.

Goodwin, P. (2010). The Holt-Winters approach to exponential smoothing: 50 years old and going strong. Foresight, 19(19), 30-33.

Gössling, S., & Schweiggartd, N. (2022) Two years of COVID-19 and tourism: What we learned, and what we should have learned. Journal of Sustainable Tourism, 30(4), 915-931.

Gössling, S., Scott, D., & Hall, C. M. (2020). Pandemics, tourism and global change: A rapid assessment of COVID-19. Journal of Sustainable Tourism, 29, 1-20.

Gretzel, U., Fuchs, M., Baggio, R., Hoepken, W., Law, R., Neidhardt, J., Pesonen, J., Zanker, M., & Xiang, Z. (2020). e-Tourism beyond COVID-19: A call for transformative research. Information Technology & Tourism, 22, 187-203.

Jaipuria, S., Parida, R., & Ray, P. (2020). The impact of COVID-19 on the tourism sector in India. Tourism Recreation Research, 46(2), 245-260.

Kalekar, P. S. (2004). Time series forecasting using the Holt-Winters exponential smoothing. Kanwal Rekhi School of Information Technology, 4329008(13), 1-13.

Keadplang, K. (2018). Competitiveness Development of Wellness Tourism Destination toward VIP Xperience for Bleisure Tourists. Veridian E-Journal, 11(5), 698-708.

Landmesser, J. M. (2021). The use of the dynamic time warping (DTW) method to describe the COVID-19 dynamics in Poland. Oeconomia Copernicana, 12(3), 539-556.

Lee, Ch., Chen, M. P., Wu, W., & Xing, W. (2021). The impacts of ICTs on tourism development: International evidence based on a panel quantile approach. Information Technology and Tourism, 23, 509-547.

Li, X., Lin, Z., & Xiao, S. (2022). Using social media big data for tourist demand forecasting: A new machine learning analytical approach. Journal of Digital Economy, 1, 32-43.

Mamirkulova, G., Mi, J., Abbas, J., Mahmood, S., Mubeen, R., & Ziapour, A. (2020). New Silk Road infrastructure opportunities in developing the tourism environment for residents better quality of life. Global Ecology and Conservation, 24, e01194.

McCabe, S., & Qiao, G. (2020). A review of research into social tourism: Launching the Annals of Tourism Research Curated Collection on Social Tourism. Annals of Tourism Research, 85, 103103,1-25.

Meadows, C. W., Meadows, C. Z., Tang, L., & Liu, W. (2019). Unraveling public health crises across stages: Understanding Twitter emotions and message types during the California measles outbreak. Communication Studies, 70(4), 453-469.

Medical Tourism Magazine (2022). Wellness travel will be the focus of the post-pandemic era. Retrieved December 31, 2022, from https://www.magazine.medicaltourism.com/article/wellness-travel-will-be-the-focus-in-the-post-pandemic-era

Mei, Z., Qiu, H., Feng, C., & Cheng, Y. (2019). Research on a forecasting model of tourism traffic volume in theme parks in China. Transportation Safety and Environment, 1(2), 135-144.

Ndou, V., Mele, G., Hysa, E., & Manta, O. (2022). Exploiting Technology to Deal with the COVID-19 Challenges in Travel & Tourism: A Bibliometric Analysis. Sustainability, 14, 5917.

Onuferová, E., Čabinová, V., & Vargová, T. D. (2020). Analysis of modern methods for increasing and managing the financial prosperity of businesses in the context of performance: a case study of the tourism sector in Slovakia. Oeconomia Copernicana, 11(1), 95-116.

Page, S., Yeoman, I., Munro, C., Connell, J., & Walker, L. (2006). A case study of best practice ─ Visit Scotland's prepared response to an influenza pandemic. Tourism Management, 27(3), 361-393.

Přívara, A. (2022). Economic growth and labour market in the European Union: lessons from COVID-19. Oeconomia Copernicana, 13(2), 355-377.

Rashad, A. S. (2022). The Power of Travel Search Data in Forecasting the Tourism Demand in Dubai. Forecasting, 4(3), 1-11.

Rittichainuwat, B. N., & Chakraborty, G. (2009). Perceived travel risks regarding terrorism and disease: The case of Thailand. Tourism management, 30(3), 410-418.

Sharma, G. D., Thomas A., & Paul, J. (2021). Reviving tourism industry post-COVID-19: A resilience-based framework. Tourism Management Perspectives, 37, 100786.

Sigala, M. (2020). Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research. Journal of Business Research, 117, 312-321.

Stankova, M., Tsvetkov, T., & Ivanova, L. (2019). Tourist development between security and terrorism: empirical evidence from Europe and the United States. Oeconomia Copernicana, 10(2), 219-237.

Statista. (2022). Employment loss in travel and tourism due to the coronavirus (COVID-19) pandemic worldwide from 2020 to 2022 from region. Retrieved December 27, 2022, from https://www.statista.com/statistics/1104835/coronavirus-travel-tourism-employment-loss/

Tayman, J., & Swanson, D. A. (1999). On the validity of MAPE as a measure of population forecast accuracy. Population Research and Policy Review, 18, 299-322.

The World Bank. (2020). International tourism, number of arrivals. Retrieved December 27, 2022, from https://data.worldbank.org/indicator/ST.INT.ARVL

Tourism sector recovery plan. COVID-19 Response. (2020). South Africa. Retrieved December 31, 2022, from https://www.gov.za/sites/default/files/gcis_document/202008/tourismrecoveryplan.pdf

UNWTO. (2020). International Tourism Highlights 2020 Edition. Retrieved December 27, 2022, from https://www.e-unwto.org/doi/book/10.18111/9789284422456

WTTC. (2020). Latest Research from WTTC Shows a 50% Increase in Jobs at Risk in Travel and Tourism.

Xiao, Y., Tian, X. T., Liu, J. J., Cao, G. H., & Dong, Q. X. (2020). Tourism Traffic Demand Prediction Using Google Trends Based on EEMD-DBN. Engineering, 12, 194-215.

Zeng, B., Carter, R. W., & De Lacy, T. (2005). Short-term perturbations and tourism effects: The case of SARS in China. Current Issues in Tourism, 8(4), 306-322.

Zenker, S., & Kock, F. (2020). The coronavirus pandemic–A critical discussion of a tourism research agenda. Tourism management, 81, 104164.

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Published

2025-12-17
Received 2025-10-17
Accepted 2025-11-02
Published 2025-12-17