A Predictive Model for Confirmed Cases of COVID-19 in Nigeria
Keywords:COVID-19, Pandemic, ARIMA, Box-Jenkin, forecasting
Background and Objectives: COVID-19 pandemic globally remains a major problem affecting every aspect of human endeavors with Nigeria, not an exception. The number of confirmed cases of COVID-19 in Nigeria was 3,912 this study builds an ARIMA model for forecasting the confirmed cases of COVID-19 in Nigeria based on Box-Jenkins methodology. Materials and Method: Data on confirmed cases of COVID-19 in Nigeria were obtained from the Nigeria Centre for Disease Control (NCDC). The stationarity of the data was determined using the Augmented Dickey-Fuller (ADF) test and stationarity was achieved after taking the natural log transformation and first differencing. Based on Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, three ARIMA models were identified [ARIMA (2,1,0), ARIMA (0,1,2) and ARIMA (2,1,2)]. The fitness performance of the models was compared using R2 and normalized Bayesian Information Criteria while the forecasting accuracy was compared using Root Mean Square Error (RMSE). Results: Results showed that among the ARIMA models, the ARIMA (2,1,0) outperformed other proposed models both in terms of fitness and forecasting accuracy and hence the ARIMA (2,1,0) was recommended for forecasting confirmed cases of COVID-19 in Nigeria. Hence, the ARIMA (2,1,0) model was then used to project the confirmed cases of COVID-19 in Nigeria for the next two weeks (9/05/2020 to 2/05/2020). The forecasts showed an upsurge in the confirmed cases of COVID-19 in Nigeria if the current relaxation of the lockdown continues. Conclusion: This finding has implication for the need for the Nigeria government to ensure that COVID-19 preventive measures such as social distancing, use of face mask, regular hand washing with soap and water or alcohol-based sanitizer as well as the restriction of movement are sustained to possibly hurt these projected increase in the confirmed cases of COVID-19 in Nigeria in order to prevent the collapse of health systems.
Anastassopoulou, C., L. Russo, A. Tsakris and C. Siettos, 2020. Data-based analysis, modeling and forecasting of the COVID-19 outbreak. PLoS ONE, 15(3): e0230405. DOI: 10.1371/ journal.pone.0230405.
Yonar, H, A. Yonar, M.A. Tekindal and M. Tekindal, 2020. Modeling and forecasting for the number of cases of the COVID-19 pandemic with the curve estimation models, the Box-Jenkins and exponential smoothing methods. EJMO, 4(2):160–165. DOI: 10.14744/ejmo.2020.28273.
Johns Hopkins University Centre for System Science and Engineering, 2020. Coronavirus (COVID-19) cases. 2020. Available from http://github.com/CSSEGISandData/COVIDQ3.
World Health Organization (WHO), 2020. Novel coronavirus(2019-nCoV). Situation report 21. Geneva, Switzerland: World Health Organization; 2020; 2020. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200210-sitrep-21-ncov.pdf?sfvrsn=947679ef_2.
Papastefanopoulos, V., P. Linardatos and S. Kotsiantis, 2020. COVID-19: A comparison of time series methods to forecast percentage of active cases per population. Appl. Sci., 10, 3880; DOI:10.3390/app10113880.
NCDC, 2020. Nigeria Centre for Disease Control. Available from: https://ncdc.gov.ng.
Tran, T.T., L.T. Pham and Q.X. Ngo, 2020. Forecasting epidemic spread of SARS-COV-2 using ARIMA model (case study: Iran). Global J. of Envr. Sci and Mgt, 6: 1-10. DOI: 10.22034/GJESM.2019.06.SI.01.
Gift, R. A, and R. M. Olalekan, 2020. Access to electricity and water in Nigeria: A panacea to slow the spread of Covid-19. Open Access J Sci; 4(2):3-4. DOI: 10.15406/oajs.2020.04.00148.
Gift RA, Olalekan RM, Owobi OE, Oluwakemi RM, Anu B, Funmilayo AA (2020). Nigerians crying for availability of electricity and water: a key driver to life coping measures for deepening stay at home inclusion to slow covid-19 spread. Open Access Journal of Science. 2020;4(3):69‒80. DOI: 10.15406/oajs.2020.04.00155.
Box, G. E. P., and Jenkins, G. M. (1976). Time series analysis: Forecasting and control, Holden-Day Inc. USA., Pages: 18. Source: https://dl.acm.org/citation.cfm?id=574978.
Clement, E.P. 2014. Using normalized Bayesian Information Criterion (BIC) to improve Box - Jenkins model building. American J. of Maths. and Stat., 4(5): 214-221. DOI: 10.5923/j.ajms.20140405.02.
Petropoulos, F, S. Makridakis, 2020. Forecasting the novel coronavirus COVID-19. PLoS ONE 15(3): e0231236. DOI: 10.1371/journal.pone.0231236.
Jiang, X., B. Zhao, and J. Cao, 2020. Statistical analysis on COVID-19. Biomed J Sci & Tech Res., 26(2): 19716-19727. DOI: 10.26717/BJSTR.2020.26.004310.
Sujath, R., J.M. Chatterjee, A.E. Hassanien, 2020. A machine learning forecasting model for COVID-19 pandemic in India. Stochastic Environmental Research and Risk Assessment 34:959–972. DOI: 10.1007/s00477-020-01827-8.(0123456789
Al-qaness, M.A., A. A. Ewees, F. Hong and El Aziz, M.A., 2020. Optimization method for forecasting confirmed cases of COVID-19 in China. J. Clin. Med. 2020, 9, 674; DOI:10.3390/jcm9030674.
Cheng, Z.J. and J. Shan, 2020. 2019 Novel coronavirus: where we are and what we know. Int J. Infect., 94: 44-48. DOI: 10.1016/j.ijid.2020.03.004.
Wu, J. T. K. Leung and G.M. Leung, 2020. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet, 29 (395):689-697. DOI: 10.1016/S0140-6736(20)30260-9.
Benvenuto, D., M. Giovanetti, L. Vasallo, S. Angeletti and M. Ciccozzi, 2020. Application of the ARIMA model on COVID-2019 epidemic data set. Data in Brief, 29, 105340. DOI: 10.1016/j.dib.2020.105340.