Modelling Daily Covid-19 Cases In Kenya Using ARIMA And SARIMA Models

Abstract

ABSTRACT Severe Acute Respiratory Syndrome is the primary cause of the pandemic coronavirus disease. The first case was reported in Wuhan, China, on 30th December, 2019 with the first case on 13th March, 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. This study therefore aimed at mod- elling daily COVID-19 cases in Kenya, using an Autoregressive Integrated Moving Average (ARIMA) model and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The specific objectives were: to fit an Autoregressive Integrated Moving Average (ARIMA) model, to fit a SARIMA model, to validate the model and to determine the forecast of COVID-19 cases. The World Health Organization was used as the source of secondary data dating from 13th March, 2020 to 30th April, 2023. These data was analyzed using R software. The training data was found to be non-stationary using a test known as Augmented Dickey Fuller, and it was differ- enced seasonally to make it stationary. The methodology used to fit the models was Box-Jenkins which uses the least AIC and BIC as its fitting criteria. The data revealed weekly seasonality hence invalidating the ARIMA model. SARIMA model was fitted and model validation using test data was done. The model with the least forecast errors was selected. The SARIMA(1,0,1)(2,1,2)7 was selected with the least AIC = 2082.5, MAE = 2.9867, RMSE = 4.5815. Using the model, a ninety days forecast into the fu- ture was generated based on daily COVID-19 data. These forecasts will greatly create awareness of the trend and seasonality of this disease and therefore can be very useful to the health care providers as well as the government for purpose of planning, policy formulation, evaluation and resource allocation. This study recommends a compara- tive study on Bayesian SARIMA and SARIMA model to be perfomed, consideration of the possible change in probabilistic structures of the data and fitting of the BATS and TBATS models to the data