ResearchBib Share Your Research, Maximize Your Social Impacts
Sign for Notice Everyday Sign up >> Login


Journal: International Journal of Electrical Engineering and Technology (IJEET) (Vol.12, No. 8)

Publication Date:

Authors : ; ;

Page : 105-114

Keywords : ARIMA; COVID-19; prediction; pandemic; infections; respiratory system;

Source : Downloadexternal Find it from : Google Scholarexternal


The study was designed to predict the COVID-19 infections in Kenya. Due to increased COVID-19 infections, there has been a scarcity of resources like quarantine centers and personal protective equipment's for the medics. Therefore, effective planning was required by the Kenyan Government to ensure resources are available to combat the spread of the SARS-COV-2 virus. Therefore, the Autoregressive Integrated Moving Average (ARIMA) model was applied to predict the COVID-19 infections in Kenya. Quantitative data from the Kenya ministry of health was used. The analysis entailed descriptive statistics and the time series analysis. From the scatter plot, a linear relationship between the infection cases and the sample tested was observed implying that the number of infection cases increases with increase in the sample size. The ARIMA model was fitted in R software and the ARIMA (0,1,2) was identified as the best prediction model for the COVID-19 infection in Kenya based on the Akaike information criterion. The prediction was done for a period of 150 days and the infectivity cases were observed to increase at constant rate then, the curve eventually flattens. This implied that the ARIMA is a better prediction model for the COVID-19 infections. The study recommended that Kenyans should observe the World Health Organization's guidelines to help reduce the infectivity rate. The government of Kenya should provide affordable face masks, and personal protective equipment for the medics. The government should encourage mass testing and quarantine all the infected people to curb the surging COVID-19 infection.

Last modified: 2021-08-27 22:22:33