Journal of Information Systems Engineering and Management

Journal of Information Systems Engineering and Management, 2024 - Volume 9 Issue 4, Article No: 30195
https://doi.org/10.55267/iadt.07.15132

Published Online: 09 Sep 2024

Views: 316 | Downloads: 209

Influenza is a highly contagious respiratory disease and is still a serious threat to public health all over the world. Forecasting techniques help in monitoring seasonal influenza and other influenza-like diseases and also in managing resources appropriately to formulate vaccination strategies and choose appropriate public health measures to reduce the impact of the disease. The aim of this investigation is to forecast the monthly incidence of seasonal flu in Saudi Arabia for the years 2020 and 2021 using the XGBoost model and compare it with ARIMA and SARIMA models. The results show that the XGBoost model has the lowest values MAE, MAE, and RMSE compared to the ARIMA and SARIMA models and the highest value of R-squared (R²). This study compares the accuracy of the XGBoost model with ARIMA and SARIMA models in providing a forecast of the number of monthly seasonal influenza cases. These results confirm the notion that the XGBoost model has a higher accuracy of prediction than that of the ARIMA and SARIMA models, mainly due to its capacity to capture complex nonlinear relationships. Therefore, the XGBoost model could predict monthly occurrences of seasonal influenza cases in Saudi Arabia.

Seasonal Influenza ARIMA SARIMA XGBoost Machine Learning Public Health Prediction

• Alharbi, S. A., Abdoon, M. A., Saadeh, R., Alsemiry, R. D., Allogmany, R., Berir, M., & EL Guma, F. (2024). Modeling and analysis of visceral leishmaniasis dynamics using fractional‐order operators: A comparative study. Mathematical Methods in the Applied Sciences, 47(12), 9918–9937. doi:10.1002/mma.10101
• Ali, M., Alzahrani, S. M., Saadeh, R., Abdoon, M. A., Qazza, A., Al-kuleab, N., & EL Guma, F. (2024a). Modeling COVID-19 spread and non-pharmaceutical interventions in South Africa: A stochastic approach. Scientific African, 24, e02155. doi:10.1016/j.sciaf.2024.e02155
• Ali, M., Guma, F. E., Qazza, A., Saadeh, R., Alsubaie, N. E., Althubyani, M., & Abdoon, M. A. (2024b). Stochastic modeling of influenza transmission: Insights into disease dynamics and epidemic management. Partial Differential Equations in Applied Mathematics, 100886.
• Aljandali, A. (2017). The Box-Jenkins methodology. In Multivariate Methods and Forecasting with IBM® SPSS® Statistics. Cham, Switzerland: Springer.
• Almutairi, D. K., Abdoon, M. A., Salih, S. Y. M., Elsamani, S. A., Guma, F. E., & Berir, M. (2023). Modeling and analysis of a fractional visceral leishmaniosis with Caputo and Caputo–Fabrizio derivatives. Journal of the Nigerian Society of Physical Sciences, 1453-1453. doi:10.46481/jnsps.2023.1453
• Alsobhi, A. (2022). Prediction of COVID-19 disease by ARIMA model and tuning hyperparameter through GridSearchCV. Emerging Technologies in Data Mining and Information Security, 543–551. doi:10.1007/979814051_54
• Alsubaie, N., EL Guma, F., Boulehmi, K., Al-kuleab, N., & Abdoon, M. A. (2024). Improving influenza epidemiological models under Caputo fractional-order calculus. Symmetry, 16(7), 929. doi:10.3390/sym16070929
• Alzahrani, S. M., Saadeh, R., Abdoon, M. A., Qazza, A., Guma, F. E., & Berir, M. (2024). Numerical simulation of an influenza epidemic: Prediction with fractional SEIR and the ARIMA model. Applied Mathematics & Information Sciences, 18(1), 1-12. doi:10.18576/amis/180101
• Anderson, O. D. (1977). The Box-Jenkins approach to time series analysis. RAIRO-Operations Research, 11(1), 29.‏‏
• ArunKumar, K. E., Kalaga, D. V., Kumar, C. M. S., Chilkoor, G., Kawaji, M., & Brenza, T. M. (2021). Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Applied Soft Computing, 103, 107161.
• Arwaekaji, M., Sillabutra, J., Viwatwongkasem, C., & Soontornpipit, P. (2022). Forecasting influenza incidence in public health region 8 Udonthani, Thailand by SARIMA model. Current Applied Science and Technology, 22(4). doi:10.55003/cast.2022.04.22.015
• Badar, N., Ikram, A., Salman, M., Saeed, S., Mirza, H. A., Ahad, A., . . . Farooq, U. (2024). Evolutionary analysis of seasonal influenza A viruses in Pakistan 2020–2023. Influenza and Other Respiratory Viruses, 18(2). doi:10.1111/irv.13262
• Bezerra, A. K. L., & Santos, É. M. C. (2020). Prediction of the daily number of confirmed cases of COVID-19 in Sudan with ARIMA and Holt-Winters exponential smoothing. International Journal of Development Research, 10(08), 394039413.
• Box, G. (2013). Box and Jenkins: Time series analysis, forecasting and control. In A Very British Affair: Six Britons and the Development of Time Series Analysis During the 20th Century (p. 16215). London, UK: Palgrave Macmillan.‏
• Chen, Q., Zheng, X., Shi, H., Zhou, Q., Hu, H., Sun, M., . . . Zhang, X. (2024). Prediction of influenza outbreaks in Fuzhou, China: Comparative analysis of forecasting models. BMC Public Health, 24(1). doi:10.1186/s1288021858x
• Chen, Y., Leng, K., Lu, Y., Wen, L., Qi, Y., Gao, W., ... & Dong, J. (2020). Epidemiological features and time-series analysis of influenza incidence in urban and rural areas of Shenyang, China, 2010–2018. Epidemiology & Infection, 148, e29. doi:10.1017/S0950268820000151
• Dancer, D., & Tremayne, A. (2005). R-squared and prediction in regression with ordered quantitative response. Journal of Applied Statistics, 32(5), 483–493. doi:10.1080/02664760500079423
• Dandachi, I., Alrezaihi, A., Amin, D., AlRagi, N., Alhatlani, B., Binjomah, A., . . . Aljabr, W. (2024). Molecular surveillance of influenza A virus in Saudi Arabia: Whole-genome sequencing and metagenomic approaches. Microbiology Spectrum, 12(8). doi:10.1128/spectrum.006624
• Devlin, R. K. (2008). The influenza virus. In J. K. Silver (Ed.), Influenza (pp. 1–20). doi:10.5040/9798400670053
• EL Guma, F. (2024). Comparative analysis of time series prediction models for visceral leishmaniasis: based on SARIMA and LSTM. Applied Mathematics & Information Sciences, 18(1), 125-132. doi:10.18576/amis/180113
• EL Guma, F., Abdoon, M. A., Qazza, A., Saadeh, R., Arishi, M. A., & Degoot, A. M. (2024). Analyzing the impact of control strategies on visceral leishmaniasis: A mathematical modeling perspective. European Journal of Pure and Applied Mathematics, 17(2), 1213–1227. doi:10.29020/nybg.ejpam.v17i2.5121
• EL Guma, F., Musa, A. G. M., Alkhathami, F. D., Saadeh, R., & Qazza, A. (2023). Prediction of visceral leishmaniasis incidences utilizing machine learning techniques. In 2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) (pp. 1-6). Zarqa, Jordan: IEEE.
• Hoque, K. E., & Aljamaan, H. (2021). Impact of hyperparameter tuning on machine learning models in stock price forecasting. IEEE Access, 9, 163815–163830. doi:10.1109/access.2021.3134138
• Kaur, J., Parmar, K. S., & Singh, S. (2023). Autoregressive models in environmental forecasting time series: A theoretical and application review. Environmental Science and Pollution Research, 30(8), 19617–19641. doi:10.1007/s11350225149
• Khan, D. R., Patankar, A. B., & Khan, A. (2024). An experimental comparison of classic statistical techniques on univariate time series forecasting. Procedia Computer Science, 235, 2730–2740. doi:10.1016/j.procs.2024.04.257
• Kumar, D. S., Thiruvarangan, B. C., Vishnu, A., Devi, A. S., & Kavitha, D. (2022). Analysis and prediction of stock price using hybridization of SARIMA and XGBoost. In 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) (pp. 1-4). Chennai, India: IEEE.
• Kuran, F., Tanırcan, G., & Pashaei, E. (2023). Performance evaluation of machine learning techniques in predicting cumulative absolute velocity. Soil Dynamics and Earthquake Engineering, 174, 108175. doi:10.1016/j.soildyn.2023.108175
• Li, W., Yin, Y., Quan, X., & Zhang, H. (2019). Gene expression value prediction based on XGBoost algorithm. Frontiers in Genetics, 10, 1077. doi:10.3389/fgene.2019.01077
• Luo, J., Zhang, Z., Fu, Y., & Rao, F. (2021). Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms. Results in Physics, 27, 104462. doi:10.1016/j.rinp.2021.104462
• Lv, C. X., An, S. Y., Qiao, B. J., & Wu, W. (2021). Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model. BMC Infectious Diseases, 21(1). doi:10.1186/s1287020650y
• Man, H., Huang, H., Qin, Z., & Li, Z. (2023). Analysis of a SARIMA-XGBoost model for hand, foot, and mouth disease in Xinjiang, China. Epidemiology and Infection, 151. doi:10.1017/s0950268823001905
• Mills, T. C. (2019). ARIMA models for nonstationary time series. In Applied Time Series Analysis (pp. 57–69). doi:10.1016/b970-1813116.00001
• Nelson, B. K. (1998). Time series analysis using autoregressive integrated moving average (ARIMA) models. Academic Emergency Medicine, 5(7), 739–744. doi:10.1111/j.1552712.1998.tb02493.x
• Nelson, M. I., & Holmes, E. C. (2007). The evolution of epidemic influenza. Nature Reviews Genetics, 8(3), 196–205. doi:10.1038/nrg2053
• Peixeiro, M. (2022). Time series forecasting in Python. Shelter Island, NY: Simon and Schuster.
• Song, H. (2017, May 21). Review of Time Series Analysis and Its Applications With R Examples (3rd Edition) [Review of the book Time Series Analysis and Its Applications With R Examples (3rd Edition), by R. H. Shumway & D. S. Stoffer]. Structural Equation Modeling: A Multidisciplinary Journal, 24(5), 800–802. doi:10.1080/10705511.2017.1299578
• Sroka, Ł. (2024). Simulation analysis of artificial neural network and XGBoost algorithms in time series forecasting, Scientific Papers of Silesian University of Technology Organization and Management Series, 2024(195). doi:10.29119/1643466.2024.195.34
• Tenepalli, D., & TM, N. (2024). A systematic review on IoT and machine learning algorithms in e-healthcare. International Journal of Computing and Digital Systems, 16(1), 27294.
• World Health Organization. (2023). Global Influenza Surveillance and Response System (GISRS). Retrieved from https://www.who.int/initiatives/global-influenza-surveillance-and-response-system
• Yasmin, S., & Moniruzzaman, M. (2024). Forecasting of area, production, and yield of jute in Bangladesh using Box-Jenkins ARIMA model. Journal of Agriculture and Food Research, 16, 101203.‏
• Yenilmez, İ., & Mugenzi, F. (2023). Estimation of conventional and innovative models for Rwanda's GDP per capita: A comparative analysis of artificial neural networks and Box–Jenkins methodologies. Scientific African, 22, e01902.‏
• Zhang, L., Bian, W., Qu, W., Tuo, L., & Wang, Y. (2021). Time series forecast of sales volume based on XGBoost. Journal of Physics: Conference Series, 1873(1), 012067. doi:10.1088/1746596/1873/1/012067
• Zhao, Z., Zhai, M., Li, G., Gao, X., Song, W., Wang, X., . . . Qiu, L. (2023). Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China. BMC Infectious Diseases, 23(1), 71.

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.