Introduction: The novel coronavirus infection has become a global threat affecting almost every country in the world. As a result, it has become important to understand the disease trends in order to mitigate its effects. The aim of this study is firstly to develop a prediction model for daily confirmed COVID-19 cases based on several covariates, and secondly, to select the best prediction model based on a subset of these covariates. Methodology: This study was conducted using daily confirmed cases of COVID-19 collected from the official Ministry of Health, Malaysia (MOH) and John Hopkins University websites. An Autoregressive Integrated Moving Average (ARIMA) model was fitted to the training data of observed cases from 22 January to 31 March 2020, and subsequently validated using data on cases from 1 April to 17 April 2020. The ARIMA model satisfactorily forecasted the daily confirmed COVID-19 cases from 18 April 2020 to 1 May 2020 (the testing phase). Results: The ARIMA (0,1,0) model produced the best fit to the observed data with a Mean Absolute Percentage Error (MAPE) value of 16.01 and a Bayes Information Criteria (BIC) value of 4.170. The forecasted values showed a downward trend of COVID-19 cases until 1 May 2020. Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model. Conclusions: This study finds that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Malaysia.

Background: COVID-19 was declared a pandemic by the World Health Organization on 11 March 2020. From the beginning of the pandemic, there was no effective pharmaceutical intervention to halt or hold up the spread of this novel disease. Therefore, most countries, including Malaysia, resorted to break the chain of transmission by restricting population mobility through the implementation of the Movement Control Order (MCO). We aim to determine the population mobility trend across the various phases of the MCO during the COVID-19 pandemic in Malaysia by studying the confirmed COVID-19 cases with the Google mobility data. Methodology: The average mobility percentage changes in Retail and Recreation, Grocery and Pharmacy, Parks, Transit Stations, and Workplaces were the components studied in relation to the various MCO phases and daily COVID-19 confirmed cases. The percentage difference was calculated by subtracting the average percentage changes for each MCO phases from the pre-MCO level. Additionally, the percentage difference was also calculated for inter-MCO phases as well. Results: The average mobility percentage changes reduced most drastically during the MCO phases across all the mobility components as compared to the other phases. The average mobility percentage changes in comparison to the pre-MCO levels across Retail and Recreation, Grocery and Pharmacy, Parks, Transit Stations, and Workplaces was −45.8%, −10.6%, −27.7%, −60%, and −34.3%, respectively. In addition, the average mobility percentage changes increased the most during CMCO as compared to MCO. Discussions: Malaysia implemented multiple measures to contain the COVID-19 pandemic since January 2020, culminating in the execution of the MCO. Though doubts on the effectiveness of the MCO were raised at the early stage of its implementation as mass movements persisted, strict enforcement and improved awareness of the impacts of COVID-19 brought significant improvement in compliance, which has been deemed the main reason behind the decrease in new COVID-19 cases since mid-April of 2020. Conclusion: Based on the downtrends of new and active COVID-19 cases, it can be concluded that the MCO has been effective, provided that compliance to the MCO is maintained. This study could serve to a certain degree to governments and policy makers as a tool to consider the relaxation of the lockdown conditions.