Forecasting of New Cases of COVID-19 in Nigeria Using Autoregressive Fractionally Integrated Moving Average Models
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Published
Sep 26, 2020
Page:
135-146
Main Article Content
Olumide Sunday Adesina
Department of Mathematical Sciences, Redeemer’s University, Nigeria.
Samson Adeniyi Onanaye
Department of Mathematical Sciences, Redeemer’s University, Nigeria.
Dorcas Okewole
Department of Mathematical Sciences, Redeemer’s University, Nigeria.
Amanze C. Egere
Department of Mathematical Sciences, Redeemer’s University, Nigeria.
Abstract
The emergence of global pandemic known as COVID-19 has impacted significantly on human lives and measures have been taken by government all over the world to minimize the rate of spread of the virus, one of which is by enforcing lockdown. In this study, Autoregressive fractionally integrated moving average (ARFIMA) Models was used to model and forecast what the daily new cases of COVID-19 would have been ten days after the lockdown was eased in Nigeria and compare to the actual new cases for the period when the lockdown was eased. The proposed model ARFIMA model was compared with ARIMA (1, 0, 0), and ARIMA (1, 0, 1) and found to outperform the classical ARIMA models based on AIC and BIC values. The results show that the rate of spread of COVID-19 would have been significantly less if the strict lockdown had continued. ARFIMA model was further used to model what new cases of COVID-19 would be ten days ahead starting from 31st of August 2020. Therefore, this study recommends that government should further enforce measures to reduce the spread of the virus if business must continue as usual.
Keywords:
COVID-19, ARFIMA, time series, lockdown, Nigeria.
Article Details
How to Cite
Adesina, O. S., Onanaye, S. A., Okewole, D., & Egere, A. C. (2020). Forecasting of New Cases of COVID-19 in Nigeria Using Autoregressive Fractionally Integrated Moving Average Models. Asian Research Journal of Mathematics, 16(9), 135-146. https://doi.org/10.9734/arjom/2020/v16i930226
Section
Original Research Article
References
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Alemneh HT, Tilahun GT. Mathematical modeling and optimal control analysis of COVID-19 in Ethiopia. Preprint; 2020. Available:https://doi.org/10.1101/2020.07.23.20160473
Majia C. Modes of transmission of COVID-19 outbreak- a mathematical study; 2020. Available:https://doi.org/10.1101/2020.05.16.20104315
Shah K, Abdeljawad T, Mahariq I, Jarad F. Qualitative analysis of a mathematical model in the time of COVID-19. Hindawi BioMed Research International. 2020;Article ID 5098598:(1-11). Available:https://doi.org/10.1155/2020/509859
Kyrychko YN, Blyuss KB, Brovchenko I. Mathematical modelling of dynamics and containment of COVID-19 in Ukraine; 2020. medRxiv preprint. Available:https://doi.org/10.1101/2020.07.24.20161497
Madubueze CE, Akabuike, NM, Dachollom S. The role of mathematical model in curbing COVID-19 in Nigeria. medRxiv; 2020 preprint. Available:https://doi.org/10.1101/2020.07.22.20159210
Kumar S, Sharma S, Kumari N. Future of COVID-19 in Italy: A mathematical perspective, arXiv:2004.08588v1; 2020,
Panwara H, Guptaa PK, Siddiqui BMK, Morales-Menendez R, Singha V. Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos, Solitons and Fractals. 2020;138:109944.
Gozes O, Frid-Adar M, Sagie N, Zhang H, Ji W, Greenspan H. Coronavirus Detection and Analysis on Chest CT with Deep Learning; 2020 arXiv:submit/3118613 [cs.CV] 6 Apr 2020
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine. 2020;121. Available:https://doi.org/10.1016/j.compbiomed.2020.103792.
Arumugam R, Rajathi M. A markov model for prediction of corona Virus COVID-19 in India- A Statistical Study. Journal of Xidian University; 2020;14(2):1422-1426. Available:https://doi.org/10.37896/jxu14.4/164
Alazab M, Awajan A, Mesleh A, Abraham A, Jatana V, Alhyari S. COVID-19 prediction and detection using deep learning. International Journal of Computer, Information Systems and Industrial Management Applications. 2020;12:168-181. Available:www.mirlabs.net/ijcisim/index.html
Tamhane R, Mulge S. Prediction of COVID-19 outbreak using machine learning. International Research Journal of Engineering and Technology (IRJET). 2020:7(5).
Ardabili SF, Mosavi A, Ghamisi P, Ferdinand F, Varkonyi-Koczy AR, Reuter U, Rabczuk T, Atkinson PM. COVID-19 Outbreak prediction with machine learning, medrxiv; 2020. Available:https://doi.org/10.1101/2020.04.17.20070094, Available:https://www.medrxiv.org/content/10.1101/2020.04.17.20070094v1
Fayyoumi S, Idwan S, AboShindi H. Machine learning and statistical modelling for prediction of Novel COVID-19 Patients Case Study: Jordan. International Journal of Advanced Computer Science and Applications. 2020;11(5):2020.
Adesina OS, Agunbiade DA. Time series forecast models for foreign exchange rate in a developing economy. Assumption University-eJournal of Interdisciplinary Research (AU-eJIR). 2017;2(2):11-21.
Golinski P, Spencer, Modelling the COVID-19 Epidemic Using Time Series Econometrics; 2020. Available:https://doi.org/10.1101/2020.06.01.20118612
Shrestha PM, Tha R, Neupane D, Adhikari K, Bhuju DR. Tracking and time series scenario of coronavirus: Nepal case. Applied Science and Technology Annals. 2020;1(1):42–47.
Nesteruk I. Statistics-based predictions of coronavirus epidemic spreading in mainland China. Innov Biosyst Bioeng. 2020;4(1):13–18. DOI: 10.20535/ibb.2020.4.1.195074
Firmino PRA, de Sales JP, Gonçalves Júnior J, da Silva TA. A non-central beta model to forecast and evaluate pandemics time series, Chaos, Solutions and Fractals; 2020. Available:https://doi.org/10.1016/j.chaos.2020.1102110960-0779/
Granger GWJ, Joyeux R. An introduction to long‐memory time series models and fractional differencing. Journal of Time Series Analysis, Wiley Blackwell. 1980;1(1):15-29.
Hosking JRM. Fractional differencing. Biometrika. 1981;68:165–176.
Balah B, Djeddou M. Forecasting COVID-19 new cases in Algeria using Autoregressive fractionally integrated moving average Models (ARFIMA); 2020. Available:https://doi.org/10.1101/2020.05.03.20089615
Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y. Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics. 1992;54:159–178.
Baum CF. EC 823: Applied econometrics Boston College, Spring 2013 Christopher F Baum (Bc / Diw) Arima And Arfima Models.
Sowell F. Maximum likelihood estimation of stationary univariate fractionally integrated time series models. Journal of Econometrics. 1992;53:165–188.
Core Team R. R A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2020.
Trapletti A, Hornik K. T series: Time series analysis and computational finance; 2019. R package version 0.10-47.