COVID-19 Confirmed Cases Forecasting in Malaysia Using Linear Regression and Holt's Winter Algorithm

Abstract

The 2019 coronavirus disease pandemic (COVID-19) has emerged and is spreading rapidly over the world. Therefore, it may be highly significant to have the general population tested for COVID-19. There has been a rapid surge in the use of machine learning to combat COVID-19 in the past few years, owing to its ability to scale up quickly, its higher processing power, and the fact that it is more trustworthy than people in certain medical tasks. In this study, we compared between two different models: the Holt’s Winter (HW) model and the Linear Regression (LR) model. To obtain the data set of COVID-19, we accessed the website of the Malaysian Ministry of Health. From January 24^th, 2020, through July 31^st, 2021, daily confirmed instances were documented and saved in Microsoft Excel. Case forecasts for the next 14 days were generated in the Waikato Environment for Knowledge Analysis (WEKA), and the accuracy of the forecasting models was measured by means of the Mean Absolute Percentage Error (MAPE). According to the lowest value of performance indicators, the best model is picked. The results of the comparison demonstrate that Holt's Winter showed better forecasting outcome than the Linear Regression model. The obtained result depicted the forecasted model can be further analyzed for the purpose of COVID-19 preparation and control.