Forecasting COVID-19 Disease Cases Using the SARIMA-NNAR Hybrid Model

Year 2022, Volume: 5 Issue: 1, 15 - 23, 15.03.2022

İbrahim Demir Murat Kirisci

https://doi.org/10.32323/ujma.1010490

Cited By: 1

Abstract

Background: COVID-19 is a new disease that is associated with high morbidity that has spread around the world. Credible estimating is crucial for control and prevention. Nowadays, hybrid models have become popular, and these models have been widely implemented. Better estimation accuracy may be attained using time-series models. Thus, our aim is to forecast the number of COVID-19 cases with time-series models.
Objective: Using time-series models to predict deaths due to COVID-19.
Design: SARIMA, NNAR, and SARIMA-NNAR hybrid time series models were used using the COVID-19 information of the Republic of Turkey Health Ministry.
Participants: We analyzed data on COVID-19 in Turkey from March 11, 2020 to February 22, 2021.
Main Measures: Daily numbers of COVID-19 confirmed cases and deaths.
Materials and methods: We fitted a seasonal autoregressive integrated moving average (SARIMA)–neural network nonlinear autoregressive (NNAR) hybrid model with COVID-19 monthly cases from March 11, 2020, to February 22, 2021, in Turkey. Additionally, a SARIMA model, an NNAR model, and a SARIMA–NNAR hybrid model were established for comparison and estimation.
Results
The RMSE, MAE, and MAPE values of the NNAR model were obtained the lowest in the training set and the validation set. Thus, the NNAR model demonstrates excellent performance whether in fitting or forecasting compared with other models.
Conclusions
The NNAR model that fits this study is the most suitable for estimating the number of deaths due to COVID-19. Hence, it will facilitate the prevention and control of COVID-19.

Keywords

COVID-19, time series, SARIMA, NNAR, forecasting, R Programming

References

• [1] Pneumonia of unknown cause — China: disease outbreak news. Geneva: World Health Organization, January 5, 2020 (https://www.who.int/csr/don/05- january-2020-pneumonia-of-unkown-cause-china/en/. opens in new tab).
• [2] E. de Wit, N. van Doremalen, D. Falzarano, V.J. Munster, SARS and MERS: recent insights into emerging coronaviruses, Nat. Rev. Microbiol. 14, (2016), 523–534.
• [3] S. Anthony, H. Fauci, L. Clifford, R. R. Redfield, Covid-19 Navigating and Uncharted, N. Engl. J. Med. 382, (2020), 1268–1269, DOI: 10.1056/NEJMe2002387
• [4] F. Jiang, Z. Zhao, X. Shao, Time series analysis of COVID-19 infection curve: A change-point perspective, Journal of Econometrics, https://doi.org/10.1016/j.jeconom.2020.07.039.
• [5] P. Wang, X. Zheng, J. Li, B. Zhu, Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics, Chaos, Solitons & Fractals, 139, (2020), 110058, https://doi.org/10.1016/j.chaos.2020.110058.
• [6] N. Feroze, Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models, Chaos, Solitons & Fractals 140, (2020), 110196, https://doi.org/10.1016/j.chaos.2020.110196
• [7] H. Lia, X.-L. Xub, D.-W. Daic, Z.-Y. Huangc, Z. Maa, Y.-J. Guan, Air pollution and temperature are associated with increased COVID-19 incidence: A time series study, International Journal of Infectious Diseases, 97, 2020, 278–282, https://doi.org/10.1016/j.ijid.2020.05.076
• [8] M.H.D.M. Ribeiro, R.G. Da Silva, V.C. Mariani, L.S. Coelho, Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil, Chaos, Solitons and Fractals, 135, (2020), 1–10.
• [9] N. Topac¸, M. Bardak, S. K. Ba˘gdatlı, M. Kiris¸ci, Early Childhood Children in COVID-19 Quarantine Days and Multiple Correspondence Analysis, Journal of Mathematical Sciences and Modelling , 3(3), (2020), 130–134. DOI: 10.33187/jmsm.808041.
• [10] M. Kiris¸ci, N. Topac¸, M. Bardak, Risk Assessment of Cognitive Development of Early Childhood Children in Quarantine Days: A New AHP Approach, Conference Proceedings of Science and Technology , 3(2) , (2020) 236-241. https://dergipark.org.tr/tr/pub/cpost/issue/58603/795537
• [11] Republic of Turkey Health Ministry, COVID-19 Information Page, https://covid19.saglik.gov.tr
• [12] R Core Team (2017). R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org
• [13] G.P. Zhang, Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, (2003), 159–175.
• [14] R. J. Hyndman, G. Athanasopoulos, ARIMA modeling in R: How does auto.arima() work In Forecasting: Principles and Practice. https: //otexts.com/fpp2/arima-r.html