ARIMA Model Estimation Based on Genetic Algorithm for COVID-19 Mortality Rates

Mohanad A. Deif (), Ahmed A. A. Solyman and Rania E. Hammam ()
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Mohanad A. Deif: Department of Bioelectronics, Modern University of Technology and Information, (MTI) University, Egypt
Ahmed A. A. Solyman: ��Department of Electrical and Electronics Engineering, Istanbul Gelisim University, Turkey
Rania E. Hammam: Department of Bioelectronics, Modern University of Technology and Information, (MTI) University, Egypt

International Journal of Information Technology & Decision Making (IJITDM), 2021, vol. 20, issue 06, 1775-1798

Abstract: This paper presents a forecasting model for the mortality rates of COVID-19 in six of the top most affected countries depending on the hybrid Genetic Algorithm and Autoregressive Integrated Moving Average (GA-ARIMA). It was aimed to develop an advanced and reliable predicting model that provides future forecasts of possible confirmed cases and mortality rates (Total Deaths per 1 million Population of COVID-19) that could help the public health authorities to develop plans required to resolve the crisis of the pandemic threat in a timely and efficient manner. The study focused on predicting the mortality rates of COVID-19 because the mortality rate determines the prevalence of highly contagious diseases. The Genetic algorithm (GA) has the capability of improving the forecasting performance of the ARIMA model by optimizing the ARIMA model parameters. The findings of this study revealed the high prediction accuracy of the proposed (GA-ARIMA) model. Moreover, it has provided better and consistent predictions compared to the traditional ARIMA model and can be a reliable method in predicting expected death rates as well as confirmed cases of COVID-19. Hence, it was concluded that combining ARIMA with GA is further accurate than ARIMA alone and GA can be an alternative to find the parameters and model orders for the ARIMA model.

Keywords: COVID-19 forecasting; ARIMA model; genetic algorithm; time-series; infection disease; pandemic (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0219622021500528

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