COVID-19'UN BİR DEVLET HASTANESİ ACİL SERVİSİ ÜZERİNDEKİ ETKİSİNİN TAHMİNİ ÜZERİNE BİR ÇALIŞMA

Year 2022, Volume: 9 Issue: 2, 1239 - 1258, 29.07.2022

Fatma Gül Altın Şeyma Çelik Eroğlu

https://doi.org/10.30798/makuiibf.1033816

Abstract

COVID-19 pandemisi, 2020 yılının ilk çeyreğinden bu yana tüm dünyada insan hayatını ciddi şekilde tehdit etmektedir. Hastaneler bu tehdide karşı ön saflarda savaşmaktadır. Bu çalışmanın amacı, bir devlet hastanesinin aylık acil servis hasta sayısının tahmin edilmesidir. Özellikle COVID-19 pandemisinin acil servis hasta sayısı üzerindeki etkisi incelenmiştir. Analizlerde Ocak 2012- Haziran 2021 (114 ay) dönemine ait veri seti kullanılırken, Box-Jenkins (B-J) ve Gri Tahminleme yaklaşımları için iki farklı veri seti oluşturulmuştur. Daha sonra SARIMA modeli, GM (1,1) ve TGM kullanılarak aylık acil servis hasta sayıları tahmin edilmiştir. Analizlerde SARIMA modeli ile hastanenin acil hasta sayısının uzun dönem trendi incelenirken, GM(1,1) ve TGM ile özellikle COVID-19 dönemine odaklanılmıştır. Elde edilen bulgular, değerlendirme kriterleri açısından en başarılı sonuçlara TGM'nin sahip olduğunu göstermektedir.

Keywords

COVID-19, Acil Servis, SARIMA, GM(1,1), TGM

A STUDY ON FORECASTING THE IMPACT OF COVID-19 ON EMERGENCY SERVICE IN A PUBLIC HOSPITAL

Abstract

The COVID-19 pandemic has seriously threatened human life all over the world since the first quarter of 2020. Hospitals have fought on the frontlines against this threat. The aim of this study is to predict the number of monthly emergency service patients for a public hospital. In particular, the impact of the COVID-19 pandemic on the number of emergency service patients was examined. While the data set for the period January 2012- June 2021 (114 months) is used in the analyses, two different data sets were created for the Box- Jenkins (B-J) and Gray Prediction approaches. Then, the number of monthly emergency service patients was predicted using the SARIMA model, GM (1,1) and TGM. In the analyses, while examining the long-term trend of the number emergency services patients’ using the SARIMA model, GM (1,1) and TGM were used to focus on the COVID-19 period. The findings suggest that the TGM has the most successful results in terms of evaluation criteria.

Keywords

COVID-19, Emergency Service, SARIMA, GM(1,1), TGM

References

• 1- Abraham, G., Byrnes, G.B. and Bain, C.A. (2009). Short-term forecasting of emergency inpatient flow. IEEE Transactions on Information Technology in Biomedicine, 13(3), 380–389. doi: 10.1109/TITB.2009.2014565
• 2- Awouda, A.E. and Mamat, R.B. (2010). Optimizing PID tuning parameters using grey prediction algorithm. International Journal of Engineering, 4(1), 26-36.
• 3- Becerra, M., Jerez, A. Aballay, B., Garces, H.O. and Fuentes, A. (2020). Forecasting emergency admissions due to respiratory diseases in high variability scenarios using time series: a case study in Chile. Science of the Total Environment, 706, 1-11. doi: 10.1016/j.scitotenv.2019.134978
• 4- Boserup, B., McKenney, M. and Elkbuli, A. (2020). The impact of the COVID-19 pandemic on emergency department visits and patient safety in the United States. American Journal of Emergency Medicine, 38(9), 1732–1736. doi: 10.1016/j.ajem.2020.06.007
• 5- Carvalho-Silva, M., Monteiro, M.T.T., de Sa-Soares, F. and Doria-Nobrega, S. (2018). Assessment of forecasting models for patient arrival at Emergency Department. Operations Research for Health Care, 18, 112–118. doi: 10.1016/j.orhc.2017.05.001
• 6- Chang, K.H., Chang, Y.C., and Tsai, I.T. (2013). Enhancing FMEA assessment by integrating grey relational analysis and the decision-making trial and evaluation laboratory approach. Engineering Failure Analysis, 31, 211-224. doi: 10.1016/j.engfailanal.2013.02.020
• 7- Cheng, Q., Tanik-Argon, N., Evans, C.S., Liu, Y., Platts-Mills, T.F. and Ziya, S. (2021). Forecasting emergency department hourly occupancy using time series analysis. American Journal of Emergency Medicine, 48, 177–182. doi: 10.1016/j.ajem.2021.04.075
• 8- Duarte, D., Walshaw, C. and Ramesh, N. (2021). A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19. Applied Sciences, 11(8), 1-17. doi: 10.3390/app11083561
• 9- Duwalage, K.I., Burkett, E., White, G., Wong, A. and Thompson, M.H. (2020). Forecasting daily counts of patient presentations in Australian emergency departments using statistical models with time-varying predictors. Emergency Medicine Australasia, 32(4), 618-625. doi: 10.1111/1742-6723.13481
• 10- Goh, C. and Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management, 23(5), 499–510. doi: 10.1016/S0261-5177(02)00009-2
• 11- Gujarati, D.N. and Porter, D.C. (1995). Basic Econometrics (Fifth Edition). US: McGraw-Hill Irwin.
• 12- Hartnett, K.P., Kite-Powell, A., DeVies, J., Coletta, M.A., Boehmer, T.K., Adjemian, J. and Gundlapalli, A.V. (2020). Impact of the COVID-19 pandemic on emergency department visits -United States, January 1, 2019–May 30, 2020. Morbidity and Mortality Weekly Report, 69(23), 1-6.
• 13- Huang, J.F., Carmeli, B., Mandelbaum, A. (2015). Control of patient flow in emergency departments, or multiclass queues with deadlines and feedback. Operations Research, 63(4), 892–908. doi: 10.1287/opre.2015.1389
• 14- Jilani, T., Housley, G., Figueredo, G., Tang, P.-S., Hatton, J. and Shaw, D. (2019). Short and long-term predictions of hospital emergency department attendances. International Journal of Medical Informatics, 129, 167-174. doi: 10.1016/j.ijmedinf.2019.05.011
• 15- Julong, D. (1989). Introduction to Grey System Theory. The Journal of Grey System, 1(1), 1-24.
• 16- Khaldi, R., El Afia, A. and Chiheb, R. (2019). Forecasting of weekly patient visits to emergency department: real case study. Procedia Computer Science, 148, 532–541. doi: 10.1016/j.procs.2019.01.026
• 17- Li, Y., Campbell, E.P., Haswell, D., Sneeuwjagt, R.J. and Venables, W.N. (2003). Statistical forecasting of soil dryness index inthe southwest of Western Australia. Forest Ecology and Management, 183(1-3), 147-157. doi: 10.1016/S0378-1127(03)00103-8
• 18- Lim, A., Anthony, P., Mun, H.C. and Wai, N.K. (2008). Assessing the accuracy of grey system theory against artificial neural network in predicting online auction closing price. Paper presented at The International Multi-Conference of Engineers and Computer Scientists 2008, Hong Kong, China, 19-21 March, 2008.
• 19- Liu, S. and Forrest, J. (2007). The current developing status on grey system theory. The Journal of Grey System, 19(2), 111-123.
• 20- Liu, S. and Lin, Y. (2006). Grey information: theory and practical applications. UK: Springer.
• 21- Lu, J., Bu, P., Xia, X., Lu, N., Yao, L. and Jiang, H. (2021). Feasibility of machine learning methods for predicting hospital emergency room visits for respiratory diseases. Environmental Science and Pollution Research, 28(23), 29701–29709. doi: 10.1007/s11356-021-12658-7
• 22- Makridakis, S. and Hibon, M. (1997). ARMA Models and the Box-Jenkins Methodology. Journal of Forecasting, 16(3), 147-163. doi: 10.1002/(SICI)1099-131X
• 23- Meciarova, Z. (2007). Modeling and forecasting seasonal time series. Journal of Information, Control and Management Systems, 5(1), 73-79.
• 24- Nayeri, N.D. and Aghajani, M. (2010). Patients’ privacy and satisfaction in the emergency department: a descriptive analytical study. Nursing Ethics, 17(2), 167-177. doi: 10.1177/0969733009355377
• 25- Ozturk, H., Sayligil, O., Musmul, A. and Ergun Acar, N. (2018). The perception of privacy in the emergency department: medical faculty hospital as a case in point. Konuralp Medical Journal, 10(1), 26-33. doi: 10.18521/ktd.356832
• 26- Pecoraro, F., Luzi, D. and Clemente, F. (2021). The efficiency in the ordinary hospital bed management: A comparative analysis in four European countries before the COVID-19 outbreak. PLOS ONE, 16(3), 1-18. doi: 10.1371/journal.pone.0248867
• 27- Rocha, C. N. and Rodrigues, F. (2021). Forecasting emergency department admissions. Journal of Intelligent Information Systems, 56(3), 509-528. doi: 10.1007/s10844-021-00638-9
• 28- Sallehuddin, R., Shamsuddin, S.M.H., Hashim, S.Z.M. and Abraham, A. (2007). Forecasting time series data using hybrid grey relational artificial neural network and auto regressive integrated moving average model. Neural Network World, 17(6), 573-605.
• 29- Tschaikowski, T., Von-Rose, A.B., Consalvo, S., Pfluger, P., Barthel, P., Spinner, C.D., Knier, B., Kanz, K.-G. and Dommasch (2020). Numbers of emergency room patients during the COVID-19 pandemic. Notfall + Rettungsmedizin Med., Online Published: 08 July 2020, 1-10. doi: 10.1007/s10049-020-00757-w
• 30- Vollmer, M.A.C., Glampson, B., Mellan, T., Mishra, S., Mercuri, L., Costello, C., Klaber, R., Cooke, G., Flaxman, S. and Bhatt, S. (2021). A unified machine learning approach to time series forecasting applied to demand at emergency departments. BMC Emergency Medicine, 21(9), 1-14. doi: 10.1186/s12873-020-00395-y
• 31- Wang, Q. (2009). Grey prediction model and multivariate statistical techniques forecasting electrical energy consumption in Wenzhou, China. Paper presented at Second International Symposium on Intelligent Information Technology and Security Informatics, Moscow, Russia, 23-25 January, 2009.
• 32- Wang, Q., Liu, S. and Yan, H. (2019). The application of trigonometric grey prediction model to average per capita natural gas consumption of households in China. Grey Systems: Theory and Application, 9(1), 19-30. doi: 10.1108/GS-08-2018-0033
• 33- Wang, Z-X., Wang, Z.-W. and Li, Q. (2020). Forecasting the industrial solar energy consumption using a novel seasonal GM (1,1) model with dynamic seasonal adjustment factors. Energy, 200, 1-11. doi: 10.1016/j.energy.2020.117460
• 34- Wargon, M., Guidet, B., Hoang, T.D. and Hejblum, G. (2009). A systematic review of models for forecasting the number of emergency department visits. Emergency Medicine Journal, 26(6), 395–399. doi: 10.1136/emj.2008.062380
• 35- WHO: Impact of COVID-19 on people's livelihoods, their health and our food systems (2020). Access address https://www.who.int/news/item/13-10-2020-impact-of-covid-19-on-people's-livelihoods-their-health-and-our-food-systems
• 36- Xiao, J., Li, Y., Xie, L., Liu, D. and Huang, J. (2018). A hybrid model based on selective ensemble for energy consumption forecasting in China. Energy, 159, 534-546. doi: 10.1016/j.energy.2018.06.161
• 37- Xu, Q., Tsui, K.-L., Jiang, W. and Guo, H. (2016). A hybrid approach for forecasting patient visits in emergency department. Quality and Reliability Engineering, 32(8), 2751-2759. doi: 10.1002/qre.2095
• 38- Xu, M., Wong, T.C. and Chin, K.S. (2013). Modeling daily patient arrivals at emergency department and quantifying the relative importance of contributing variables using artificial neural network. Decision Support Systems, 54(3), 1488-1498. doi: 10.1016/j.dss.2012.12.019
• 39- Zhang, Y., Luo, L. Yang, J., Liu, D., Kong, R. and Feng, Y. (2019). A hybrid ARIMA-SVR approach for forecasting emergency patient flow. Journal of Ambient Intelligence and Humanized Computing, 10(8), 3315-3323. doi:10.1007/s12652-018-1059-x
• 40- Zhao., Y.-F., Shou, M.-H. and Wang, Z.-X. (2020). Prediction of the number of patients infected with COVID-19 based on rolling grey verhulst models. International Journal of Environmental Research and Public Health, 17(12), 1-20. doi: 10.3390/ijerph17124582
• 41- Zhou, P., Ang, B.W. and Poh, K.L. (2006). A trigonometric grey prediction approach to forecasting electricity demand. Energy, 31(14), 2839-2847. doi: 10.1016/j.energy.2005.12.002