2024, Vol. 9, Issue 2, Part B
Detecting change and forecasting in a viral post epidemic using break for time series components (BFTSC)
Author(s): Ajare Emmanuel Oloruntoba, Olorunpomi Temitope Olubunmi, Job Eunice Ohunene and Adefabi Adekunle
Abstract: The main objective of this study is to examine the phenomenon of COVID-19 pandemic in Malaysia and presenting recommendations. COVID-19 data was obtained from the data stream of Universiti Utara Malaysia library. In the methodology, BFAST (Break for additive, Season and trend) and BFTSC (Break for time series components) was used to examine the mode of movement in the COVID-19 pandemic using R and Python software. BFTSC was created to capture the trend, seasonal, cyclical and irregular components as a combined image and to present them in a single plot. The result obtained from the components (pattern) extracted using BFTSC was suggest to be used for post Covid balancing and for future COVID-19 pandemic. Result was based on careful examination of COVID-19 pandemic and it was reveals that the pandemic may not reoccur again but gradually sliding to disappearance. Hence COVID-19 would total disappear before 2025 and by 2026 the world would have forgotten about COVID-19. Reoccurrence by 2021 can only slightly and can also be controlled by the ten recommendation listed in this study.
Pages: 142-148 | Views: 26 | Downloads: 6Download Full Article: Click Here
How to cite this article:
Ajare Emmanuel Oloruntoba, Olorunpomi Temitope Olubunmi, Job Eunice Ohunene, Adefabi Adekunle. Detecting change and forecasting in a viral post epidemic using break for time series components (BFTSC). Int J Stat Appl Math 2024;9(2):142-148.
