FORECASTING THE COVID-19 MORTALITY RATE WORLDWIDE: A COMPARISON OF UNIVARIATE MODELS
Keywords:
forecasting, COVID-19, mortality rate, mortality rate worldwide, univariate modelAbstract
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.
References
Al-Turaiki, I., Almutlaq, F., Alrasheed, H., & Alballa, N. (2021). Empirical evaluation of alternative time-series
models for COVID-19 forecasting in Saudi Arabia. International Journal of Environmental Research and Public
Health, 18(16), 8660.
De Gooijer, J. & Hyndman, R. (2006). 25 Years of Time Series Forecasting. International Journal of Forecasting,
, 443-473.
GitHub, Inc. (2021). Data on excess mortality during the COVID-19 pandemic by Our World in Data. Retrieved
from covid-19-data/public/data/excess_mortality at master · owid/covid-19-data · GitHub [Access online 14
September 2021].
Harini, S. (2020). Identification COVID-19 Cases in Indonesia with The Double Exponential Smoothing Method.
Jurnal Matematika MANTIK, 6(1), 66-75.
Khan F., Ali S., Saeed A., Kumar R., & Khan A.W. (2021). Forecasting daily new infections, deaths and recovery
cases due to COVID-19 in Pakistan by using Bayesian Dynamic Linear Models. PLOS ONE 16(6): e0253367.
Klimberg, R. K., Lawrence, K., & Lawrence, S. (2005). Forecasting sales of comparable units with data
envelopment analysis (dea). Advances in Business and Management Forecasting, 4, 201-214.
Liang, L. L., Tseng, C.H., Ho, H.J., & Wu, C.Y. (2020). Covid-19 mortality is negatively associated with test
number and government effectiveness. Scientific Reports, 10(1), 1-7.
Liu, Y.C., Kuo, R.L., & Shih, S.R. (2020). COVID-19: The first documented coronavirus pandemic in history.
Biomedical Journal, 43, 328-333.
Nazim, A. & Afthanorhan, A. (2014). A Comparison between Single Exponential Smoothing (SES), Double
Exponential Smoothing (DES), Holts (Brown) and Adaptive Response Rate Exponential Smoothing (ARRES)
Techniques in Forecasting Malaysia Population. Global Journal of Mathematical Analysis, 2, 276-280.
Sidqi, F., & Sumitra, I. (2019). Forecasting product selling using single exponential smoothing and double
exponential smoothing methods. In lop conference series: Materials Science and Engineering (Vol. 662, p.
.
World Health Organization (2020). WHO Coronavirus (COVID-19) Dashboard. Retrieved from WHO
Coronavirus (COVID-19) Dashboard | WHO Coronavirus (COVID-19) Dashboard With Vaccination Data
[Access online 15 September 2021].
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Copyright (c) 2022 Elya Sara Syuhada Azhar, Noreha Mohamed Yusof, Nur Salsabila Che Sulaiman, Siti Nuraqina Rosli
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