TURKEY'S HEALTH TOURISM DEMAND FORECAST: THE ARIMA MODEL APPROACH

Yıl 2022, Cilt: 7 Sayı: 1, 47 - 63, 23.03.2022

Necla Yılmaz

Öz

Aim: A large number of people around the world travel abroad to get health services at more affordable
prices. In terms of travel, Turkey is among the countries with a high potential to attract foreign patients.
The development of health tourism has accelerated due to many advantages such as the work quality of
the services provided in Turkey, the affordable price policy, the presence of specialist physicians, and the
geographical location. The actualization of future plans by making health tourism demand forecasting
depends on the decisions taken today. From this aspect, it is of great importance to forecast the demand
for health tourism. This study aims to predict the future status of patients who come to Turkey to receive
health services and to examine them within the scope of health tourism.
Methods: In the study, the data obtained within the scope of "Visitors Leaving by Reason of Arrival" in
TUIK Tourism Statistics were used. Data refers for quarters period of 2003q1-2019q4. ARIMA models were used to predict the future of health tourism. Analysis and estimation equations were obtained using
Eviews 10.0 package software.
Findings: ARIMA (3,0,1) was obtained as the most suitable model for the study. It is predicted that the
number of health tourists arriving in Turkey will be 734,860 in 2022 and 780,754 in 2023.
Conclusion: In the next years, Turkey has high growth potential in terms of health tourism. Considering
the expected increase in the demand for health tourism, it will be seen that Turkey has a rising trend in
terms of attracting foreign patients. The results of the study will make it easier for policymakers to make
decisions on critical issues.

Anahtar Kelimeler

health tourism, time series analysis, tourism demand forecasting, arima model

Kaynakça

• Ariyo, A.A., Adewumi A.O. and Ayo, C.K. (2014). “Stock Price Prediction Using the ARIMA Model", UKSim-AMSS 16th International Conference on Computer Modeling and Simulation, 106-112.
• Ahire, M., Fernandes, P.O., Teixeiral, J.P. (2020). “Forecasting and Estimation of Medical Tourism Demand in India”, in Rocha Á., Abreu A., de Carvalho J., Liberato D., González E., Liberato P. (Eds) Advances in Tourism, Technology and Smart Systems. Smart Innovation, Systems and Technologies, Vol. 171. Springer, Singapore.
• Akin, M.H. (2021). “An Overview of Health Tourism”, in Karaca, Ş. (Ed.), Health Tourism with a Multidisciplinary Approach Ankara: Nobel Academic Publishing.
• Aydın, D., Aypek, N., Aktepe, C., Şahbaz, R. P. and Arslan, S. (2011). “Future of Medical Tourism in Turkey”, Gazi University Faculty of Commerce and Tourism Education and the Ministry of Health General Directorate of Basic Health Services, Health Tourism Coordinatorship Joint Report, Ankara.
• Bayir, E. and Isikli, E. (2019). “Healthcare Tourism Demand and an Empirical Analysis for Istanbul”, 5th International Researchers, Statisticians and Young Statisticians Congress at: Kusadası, Turkey.
• Bennett, M., King, B. and Milner, L. (2004). The Health Resort Sector in Australia: A Positioning Study”, Journal of Vacation Marketing, 10(2): 122-137.
• Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S. and Ciccozzi, M. (2020). Data in Brief Application of the ARIMA Model on the COVID- 2019 Epidemic Dataset, Data Brief, 26(29): 105340.
• Cao, L.T., Liu, H.H, Li J, Yin, X.D, Duan, Y, and Wang, J. (2020). Relationship of Meteorological Factors and Human Brucellosis in Hebei Province, China, Science of The Total Environment, 10(703): 135491. Carrera, P.M. and Bridges, J.F. Globalization and Healthcare: Understanding Health and Medical Tourism, (2006), Expert Rev Pharmacoecon Outcomes Research, 6(4): 447–54.
• Connell, J. (2006). Medical Tourism: Sea, Sun, Sand and … Surgery, Tourism Management, 27(6).
• Cortez, N. (2008). Patients without Borders: The Emerging Global Market for Patients and the Evolution of Modern Health Care, Indiana Law Journal, 83, 71-132.
• Cuhadar, M. (2014). Modelling and Forecasting Inbound Tourism Demand to Istanbul – A Comparative Analysis, European Journal of Business and Social Sciences, 2(12): 101-119.
• Çiçekgil, Z. and Yazıcı, E. (2016). Current State and Production Projection of Chicken’s Egg in Turkey, The Journal of Agricultural Economics Researches, 2(2): 26-34.
• Dang, H.S., Huang, Y.F., Wang, C.H. and Nguyen, T.M.T. (2016). An Application of the Shortterm Forecasting with Limited Data in the Healthcare Traveling Industry, Sustainability, 8(10): 1037.
• Earnest, A., Chen, M.I., Ng, D. and Leo, Y.S. (2005). Using Autoregressive Integrated Moving Average (ARIMA) Models to Predict and Monitor the Number of Beds Occupied During a SARS Outbreak in a Tertiary Hospital in Singapore, BMC Health Services Research, 5, 1– 8.
• Gaudart, J., Touré, O., Dessay, N., Dicko, A.L., Ranque, S., Forest, L., Demongeot, J. and Doumbo, O.K., (2009). Modelling Malaria Incidence With Environmental Dependency in A Locality of Sudanese Savannah Area, Malaria Journal, 8(61): 1–12.
• Goodrich, J.N. and Goodrich, G.E. (1987). “Health-Care Tourism – An Explanatory Study”, Tourism Management, 8(3): 217-222.
• He, Z.R. and Tao, H.B. (2018). Epidemiology and ARIMA Model of Positive-Rate of Influenza Viruses among Children in Wuhan, China: A Nine Year Retrospective Study, International Journal of Infectious Diseases, 74, 61-70.
• Huang, Y.L. (2012). Forecasting the Demand for Health Tourism in Asian Countries Using a GM(1,1)-Alpha Model, Tourism & Hospitality Management, 18(2): 171-181.
• Irmak, S., Köksal, C.D. and Asilkan, Ö. (2012). Predicting Future Patient Volumes of The Hospitals By Using Data Mining Methods, International Journal of Alanya Faculty of Business, 4(1): 101-114.
• Isikli, E. SerdarAsan, S. Karadayi-Usta, S. (2020). “Predicting the Medical Tourism Demand of Turkey”, in Calisir F. and Korhan, O. (Eds) Industrial Engineering in the Digital Disruption Era. GJCIE 2019. Lecture Notes in Management and Industrial Engineering. Springer, Cham.
• Kam, H.J., Sung, J.O. and Park R.W. (2010). “Prediction of Daily Patient Numbers for a Regional Emergency Medical Center Using Time Series Analysis, Health Inform Research, 16(3): 158-165.
• Kumar, M. and Sharma, S. (2016). Forecasting Tourist in-Flow in South East Asia: A Case of Singapore. Tourism & Management Studies, 12(1):107–119.
• La Foucade, A.D., Gabriel, S., Scott, E., Theodore, K. and Metivier, C. (2019). A Survey of Selected Grey Forecasting Models with Application to Medical Tourism Forecasting, Theoretical Economics Letters, 9, 1079-1092.
• Laesser, C. (2011). Health Travel Motivation and Activities: Insights from a Mature Market— Switzerland, Tourism Review. 66, 83-89.
• Lin, C.T., Lee, I.F. and Huang, Y.L. (2009). Forecasting Thailand’s Medical Tourism Demand and Revenue from Foreign Patients, Journal of Grey System, 4, 369-376.
• Ministry of Health, (2013). “Evaluation Report on Medical Tourism in Turkey”, Republic of Turkey Ministry of Health General Directorate of Health Services, Department of Health Tourism, Ankara.
• Özer, Ö. and Sonğur, C. (2012). Turkey’s Position in the World Health Tourism and Its Economic Dimension, Mehmet Akif Ersoy University Journal of Social Sciences Institute, 4(7): 69-81.
• Rai, A., Chakrabarty, P. and Sarkar, A. (2014). Forecasting the Demand for Medical Tourism in India, Journal of Humanities and Social Science, 19(11): 22-30.
• Rathlev N.K., Chessare J, Olshaker, J., Obendorfer, D., Mehta, S.D, Rothenhaus, T., Crespo, S., Magauran, B., Davidson, K., Shemin, R., Lewis, K., Becker, J.M, Fisher, L., Guy, L., Cooper, A., Litvak, E. (2007). Time Series Analysis of Variables Associated with Daily Mean Emergency Department Length of Stay, Annals Emergency Medicine, 49(3): 265-71.
• Ross, K. (2001). Health Tourism: An Overview. HSMAI Marketing Review. https://www.hospitalitynet.org/news/4010521.html?query=%22health+tourism%22 (Date Accessed: 19.01.2022).
• Siami-Namini, S., Tavakoli, N. and Siami Namin, A. (2018). "A Comparison of ARIMA and LSTM in Forecasting Time Series," 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1394-1401.
• Singh, J. P. (2015), Healthcare Tourism in India: Opportunity and Challenges, Asian Journal of Multidimensional Research, 4(3): 37-47.
• Şen, D. (2020). A Forecast Study on Medical Tourism in Turkey, Medipol University, Graduate School of Natural and Applied Sciences, Healthcare Systems Engineering, Master's Program, Unpublished Doctoral Thesis, İstanbul.
• Tay Bayramoğlu, A. and Öztürk, Z. (2017). Inflation Forecasting Using ARIMA and Grey System Models, Journal of the Human and Social Science Researches, 6(2):760-775.
• Tontuş, O. (2016). What is Health Tourism? https://shgmturizmdb.saglik.gov.tr/TR23587/saglik-turizmi-nedir.html (Date Accessed: 15.12.2021).
• TUIK (2020). Tourism Statistics. https://data.tuik.gov.tr/ (Date Accessed: 15.12.2020). Yazıcı S., Korkmaz Yaylagül, N., Baş, A.M., Yerli, Y.C. (2018), The Perceptions of Older
• Turkish Immigrants of the Healthcare Professionals in Their Home and Host Countries, Journal of International Health Sciences and Management, 4(7), 50-59.
• Zheng, Y.L., Zhang, L.P., Zhang, X.L., Wang, K., Zheng, Y.J., 2015. Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. PLoS One 10, 1–13.