Simple prediction accuracy for covid-19 daily incident data in Malaysia using machine learning- Auto Regressive Integrated Moving Average (ARIMA), and Linear Regression (LR) Model

Introduction: The COVID-19 pandemic has spread so widely across borders and worldwide. All countries, including Malaysia, need to take immediate and aggressive action to curb and combat this virus by accurately predicting the number of new COVID-19 cases a few weeks in advance. This paper aims to present robust traditional methods that can be used to make accurate predictions for the evolution of an epidemic over the next two weeks. Methodology: There are two traditional methods for forecasting, such as ARIMA and LR. We use COVID-19 daily data in Malaysia from 1 January 2021 to 14 January 2022, and use Microsoft Excel, Minitab, and SPSS software version 26 to perform the forecasting procedure. Results: The ARIMA model showed a slight difference between the forecast value and the actual value of the new COVID-19 case compared to the forecast value for the LR model. This indicates that the efficient and usable prediction model is the ARIMA model with high adj-R2 values and low MAD, MSE, RMSE, and MAPE values. Conclusions: Accurately predicting trends is an important aspect of preventing the spread of COVID-19 outbreaks, especially in countries with large populations. Furthermore, accurate forecasts can provide feedback on whether the policies implemented effectively relieve the pressures of the national healthcare system and enable the government to evaluate different strategies for risk reduction and to regulate different policies based on projections of different areas of concern.

Measurement of Relative Efficiency Levels of Southeast Asian Countries in Dealing with the Covid-19 Pandemic Over One Year

Introduction: Handling the COVID-19 outbreak is one of the most novelties modern work is facing by many countries today. Massive outbreak needs countries efficacy and talent in creating new approaches. These approaches need to prevent the spread of the outbreak and increase the citizens' belief as the outbreak will damage the countries' functional capacity. Technical efficiency is used maximally to gain total control of the conditions. This study aims to measure the relative efficiency level of Southeast Asian countries in dealing with COVID-19 pandemic over one year. Methods: The relative efficiency level of the most successful countries in Southeast Asia in managing COVID-19 infection was determined using Frontier 4.1 through Stochastic Frontier Analysis (SFA) and Excel software. The technical efficiency of the SFA model is defined as the ratio of observed output to maximum feasible production. If the country's technical efficiency (TE) is greater than 80%, it is the most effective in Southeast Asia at managing COVID-19 infection, but if it is less than 80% or close to 0, it is inefficient. Results: This research aims at the COVID-19 epidemic in a Southeast Asian country, where the country with the highest technical efficiency score is the most efficient and indicates the country's ability to deal with the COVID-19 outbreak without any complications. Laos was ranked first (TE = 0.99901), with a technical efficiency score that was higher than that of most other Southeast Asian countries. Singapore comes in second position with a technical efficiency score of 0.99882. Brunei is in third place for COVID-19, with a technical efficiency score of 0.99870. Cambodia is in last place, with a score of 0.84675 for technical efficiency. Conclusion: Laos is the highest technical efficiency score among the southeast Asian countries. Various things that can lead to inefficiency include lack of awareness about standard operating procedures (SOP) among the causes of COVID-19 case infection in the workplace, and the community continues to increase. This condition may also be due to the lack of medication or vaccines to cure COVID-19. All communities around the world are expected to adopt standard operating procedures (SOP) such as wearing face masks, hand sanitizers, and social distance to curb the increasingly violent spread of COVID-19. © 2021 Tehran University of Medical Sciences.

Forecasting cumulative covid-19 cases in malaysia and rising to unprecedented levels

Background: COVID-19 outbreak is being studied throughout the world. Adding more analysis to date strengthening the information about the illness. Here, we analysis the data of Malaysian Ministry of Health from February 15, 2020 until January 10, 2021 was analysed using linear regression model statistical analysis with aim to forecast the trend. Materials and Methods: This study reviewed the data by Malaysia Ministry of Health from February 15, 2020, until January 10, 2021. Linear regression model statistical analysis was used for predictive modelling. The forecasting of the linear trend of the Covid-19 outbreak prediction is purposed to estimate the number of confirm cases according to the number of recoveries patients. Results: Malaysia is currently anticipating another lockdown restriction as new confirmed case of COVID-19 hit new record high. The cumulative confirmed Covid-19 cases in MCO predicted a sharp increase. At the first of March, 2021, the predicted cumulative confirmed Covid-19 cases are 319,477 cases. Conclusions: Covid-19 cases projected to 315766 by end of February 2021 with 3000-4000 daily cases predicted. Initiative and proactive measurement by Malaysian government hopefully can reduce the number of cases and flatten the infection curve. © 2021, Ibn Sina Trust. All rights reserved.