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

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 24th, 2020, through July 31st, 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

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

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 24th, 2020, through July 31st, 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

Linear regression and Holt's Winter Algorithm in forecasting daily coronavirus disease 2019 cases in Malaysia: Preliminary study

Coronavirus disease 2019 is a fatal viral disease presently sweeping the globe. COVID-19 is a novel coronavirus that produces an infectious illness. Thus, COVID-19 detection in the general population may be helpful. The involvement of machine learning in combating COVID-19 had rapidly increased because of its efficiency to scale up, faster processing capacity, and more dependable than humans in some healthcare activities. This paper will focus on two models which are Linear Regression (LR) model and Holt's Winter model. The COVID-19 dataset was taken from the Ministry of Health for Malaysia's website. Daily confirmed cases were recorded from 24th of January 2020 to 31st July 2021 and stored in Microsoft Excel. Waikato Environment for Knowledge Analysis (WEKA) software was utilized to perform the prediction of daily cases in the next 14-days and the quality of forecasting models is evaluated by two performance metrics, Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The best model is selected by the lowest value of performance metrics. The comparison shows that the forecasting result of Holt's Winter is more accurate than the LR model. The developed prediction model can help public health officials make better decisions and manage resources to decrease COVID-19 pandemic morbidity and mortality. Therefore, preparation and control procedures can be established