Forecasting the COVID-19 occurrence trend in Malaysia by using growth rate model

Kamarudin, Mohamad Azim Azfar and Che Omar, Nurul Hanis and Shahidan, Ainon Mardhiyah (2022) Forecasting the COVID-19 occurrence trend in Malaysia by using growth rate model. Mathematics Letters, 1 (2): 19. pp. 329-340. ISSN eISSN: 2948-3735

Official URL: https://sites.google.com/tmsk.uitm.edu.my/mathemat...

Abstract

In late 2019 the virus know as SARS-CoV-2 was first discovered in Wuhan, China, and has spread throughout the world which it is thought to have originated in bats according to World Health Organization (2019). Cough, fever, congestion nose, chest pain, and possibly diarrhea are signs of COVID-19. Ministry of Health Malaysia (2020) stated that the on January 25, 2020, the first coronavirus disease 2019 (COVID-19) case was identified in Malaysia. At present, Malaysia is one of the countries where the deadly coronavirus is still spreading. Thus, it is crucial to control the spreading of virus COVID-19 as well as the prediction of new COVID-19 infection during the current pandemic. The main objective of this study is to obtain the daily growth rate of COVID-19 in Malaysia by using the Growth Rate model. This model is chosen because it is simple and reliable way to depict how quickly the number of infections is fluctuating daily. The results of the study shows the forecasting trendline of growth rates for COVID-19 in Malaysia based on the 700 daily data obtained between 1/2/2020 until 31/12/2021.

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
Kamarudin, Mohamad Azim Azfar
2020899194@student.uitm.edu.my
Che Omar, Nurul Hanis
2020810126@student.uitm.edu.my
Shahidan, Ainon Mardhiyah
ainon@fskm.uitm.edu.my
Subjects: W Medicine. Health Professions > WC Communicable Diseases > General Virus Diseases
W Medicine. Health Professions > WC Communicable Diseases
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences
Journal or Publication Title: Mathematics Letters
UiTM Journal Collections: Other UiTM Journals > Mathematics Letters
ISSN: eISSN: 2948-3735
Volume: 1
Number: 2
Page Range: pp. 329-340
Keywords: COVID-19, Growth rate model, SARS-CoV-2 .
Date: 1 November 2022
URI: https://ir.uitm.edu.my/id/eprint/143934
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