Forecasting the COVID-19 mortality rate worldwide: a comparison of univariate models / Elya Sara Syuhada Azhar ... [et al.]

Azhar, Elya Sara Syuhada and Mohamed Yusof, Noreha and Che Sulaiman, Nur Salsabila and Rosli, Siti Nuraqina (2022) Forecasting the COVID-19 mortality rate worldwide: a comparison of univariate models / Elya Sara Syuhada Azhar ... [et al.]. Journal of Academia, 10. pp. 1-9. ISSN 2289-6368

Abstract

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. COVID- 19 disease was initially discovered in Wuhan, China and now spread throughout the countries. Most people infected with the virus will develop mild to moderate respiratory problems and recover without the need for special treatment. However, some people will become severely ill and require medical treatment and can cause death. COVID-19 mortality rates nationwide are increasing day by day and growing concerns. On 13 December 2021, 5,325,079 deaths worldwide were recorded. Thus, this study is regarding the mortality rate of COVID-19 using univariate forecasting techniques. The data was retrieved from GitHub Our World in Data. Holt's method was selected as the best univariate model in order to forecast the mortality rate. Holt's method shows the lowest error measures. The predicted value of the mortality rate for COVID-19 is decreasing between 1 November 2021 to 31 January 2022. The decreasing predicted value might be due to the vaccinated programs done worldwide. A further study should be done to measured the factors related to the improved spread of COVID-19.

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
Azhar, Elya Sara Syuhada
UNSPECIFIED
Mohamed Yusof, Noreha
UNSPECIFIED
Che Sulaiman, Nur Salsabila
UNSPECIFIED
Rosli, Siti Nuraqina
UNSPECIFIED
Subjects: H Social Sciences > HA Statistics > Statistical data
H Social Sciences > HF Commerce > Business education
Divisions: Universiti Teknologi MARA, Negeri Sembilan
Journal or Publication Title: Journal of Academia
UiTM Journal Collections: UiTM Journal > Journal of Academia (JoA)
ISSN: 2289-6368
Volume: 10
Page Range: pp. 1-9
Keywords: forecasting, COVID-19, mortality rate, mortality rate worldwide, univariate model
Date: 2022
URI: https://ir.uitm.edu.my/id/eprint/70053
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70053

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