Open AccessForecasting of New Cases of COVID-19 in Nigeria Using Autoregressive Fractionally Integrated Moving Average Models
Author(s) -
Olanrewaju Seun Adesina,
Samson Adeniyi Onanaye,
Dorcas Modupe Okewole,
Amanze C. Egere
Publication year - 2020
Publication title -
asian research journal of mathematics
Language(s) - English
Resource type - Journals
ISSN - 2456-477X
DOI - 10.9734/arjom/2020/v16i930226
Subject(s) - autoregressive fractionally integrated moving average , autoregressive integrated moving average , covid-19 , econometrics , autoregressive model , moving average , setar , government (linguistics) , economics , statistics , pandemic , mathematics , long memory , time series , star model , medicine , volatility (finance) , linguistics , philosophy , disease , pathology , infectious disease (medical specialty)
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.