General Letters in Mathematics

Volume 11 - Issue 2 (2) | PP: 26 - 35 Language : English
DOI : https://doi.org/10.31559/glm2021.11.2.2
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Forecasting of Covid-19 deaths in South Africa using the autoregressive integrated moving average time series model

Musyoka Kinyili ,
Maurice Wanyonyi
Received Date Revised Date Accepted Date Publication Date
25/10/2021 7/11/2021 1/12/2021 19/3/2022
Abstract
Covid-19 epidemic continues to escalate globally posing life threats to humans. Time series modeling plays a key role for the prediction of data-driven scenarios. A case for Covid-19 pandemic future numbers occurrence is one of the open forecasting scenario for application of the time series modeling. We applied the Autoregressive Integrated Moving Average (ARIMA) model to forecast the possible numbers of Covid-19 deaths in the Republic of South Africa using the previously reported data for a period of 17 months (May 2020 to September 2021). We adapted the Box-Jenkins’ methodology to step-by-step achieve the entire forecasting process. We identified the MA(1) (ARIMA(0,0,1)) as the best model based on the Akaike Information Criterion and the Bayesian Information Criterion. The forecasting done at 95% confidence interval for a period of 7 months (October 1, 2021 to April 31, 2022) indicated that the Covid-19 associated deaths in South Africa would slightly increase during the month of October 2021 but remain constant throughout the entire prediction period.


How To Cite This Article
Kinyili , M. & Wanyonyi , M. (2022). Forecasting of Covid-19 deaths in South Africa using the autoregressive integrated moving average time series model . General Letters in Mathematics, 11 (2), 26-35, 10.31559/glm2021.11.2.2

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