Forecasting of New Cases of COVID-19 in Nigeria Using Autoregressive Fractionally Integrated Moving Average Models
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.
*Author to whom correspondence should be addressed.
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.