Modelling Daily COVID-19 Cases in Kenya Using ARIMA Model

Caroline M. Kamotho, Josephine N. Ngure and Margaret W. Kinyua
Additional contact information
Caroline M. Kamotho: Department of Pure and Applied Sciences, Kirinyaga University, P.O. Box 143-10300, Kerugoya (Kenya)
Josephine N. Ngure: Department of Pure and Applied Sciences, Kirinyaga University, P.O. Box 143-10300, Kerugoya (Kenya)
Margaret W. Kinyua: Department of Mathematics Statistics and Actuarial Science,Karatina University, P.O. Box 1957-10101, Karatina (Kenya)

International Journal of Research and Innovation in Applied Science, 2023, vol. 8, issue 6, 203-211

Abstract: Severe Acute Respiratory Syndrome is the primary cause of the current pandemic coronavirus disease (COVID-19). The first case was reported in Wuhan, China, on December 30th, 2019 with the first case on 13thMarch, 2020 in Kenya. This contagious disease has become a global issue because it has resulted in millions of deaths, economic disruption leading to loss of employment and economic instability. Researchers have fitted time series models but using a short data length and without a transition. There was therefore a need to model a longer data period of daily COVID-19 cases with a transition in Kenya using theAutoregressiveIntegrated Moving Average (ARIMA) model and forecast. Secondary data from the World Health Organization from 13thMarch, 2020 to 30thApril, 2023 was analyzed using R software. The data was found to be non-stationary using the Augmented Dickey Fuller test and regular differencing was done to make it stationary. The Box-Jenkins methodology was used to fit the model of the data and afterwards forecasting was done. The ARIMA (3,1,2) was selected as the best model since it had the least Akaike Information Criterion and Bayesian Information Criterion among the possible models. Model validation using test data was done by comparing the MAE, and RMSE of the model’s forecasts and it was the best amongst the possible models with MAE = 2.77 and RMSE =2.88. The model was fitted to the daily COVID-19 data and forecasting was then done for ninety days into the future.

Date: 2023
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