The higher education system is one of the sectors that has been affected by the coronavirus disease 2019 (COVID-19) pandemic. In response to that, Universiti Teknologi MARA (UiTM) has moved all classes to open and distance learning (ODL) effective 13 April 2020. ODL has greatly assisted the process of teaching and learning during the pandemic, but the sudden adoption from face-to-face learning to ODL can lead to stress among university students. Thus, this study aimed to identify the level of stress perceived by UiTM students during ODL and explore the influence of socio-demographic characteristics on the level of perceived stress among UiTM students during ODL. A cross-sectional survey design was used which was constructed of three main parts; demographic characteristics, Perceived Stress Scale-10 (PSS-10) developed by Cohen, Kamarck, and Mermelstein (1983), and a qualitative exploratory question. The finding concluded that in general, UiTM students showed moderate stress during ODL. The factors that contributed to this are a poor internet connection, academic workload, high academic workload, deadline of assignment, internet connection, learning environment, and family problems.

COVID-19 pandemic has a huge change in worldwide education. Previous face to face method of learning have been change to open and distance learning. This study investigated students’ perceptions and challenge of Open and Distance Learning (ODL) during COVID19 in Institution of Higher Learning (IHL). Data were collected from 141 online students of IHL using Google Form. The independent sample t-test used to compare the mean of student perception and students challenges among sociodemographic. Meanwhile the Pearson correlation coefficient is also used to identify the relationship between the overall score of student perception and student challenges. As a result, the research revealed that there are significant moderate negative associations between students’ perception and students challenge.

Many efforts are currently underway around the world to improve public awareness about preventive measures and to disseminate appropriate information about COVID-19 in curbing the spread of the disease. This study aims to determine the level of awareness on control and prevention of COVID-19 among population in Malaysia. A cross-sectional study was conducted among 355 participants in between March 30th to May 21st, 2020. A set of questionnaire that consists of five main themes: (1) socio-demographics, (2) awareness, (3) knowledge, (4) attitudes, and (5) practices towards prevention and controlling COVID-19 were distributed via online using google forms. The overall Knowledge, Attitude and Practice (KAP) scores was analyzed based on Bloom’s cut-off point of 80%. The results of this study show that Malaysians’ awareness highly influence their knowledge, attitude, and practices in preventing and controlling COVID-19 spread. Although the results are reasonably good, it is recommended for further awareness to be undertaken to continuously raise the awareness level and to remove any negative stigma and attitude that consequently produce better practices to prevent the spread of this virus, so that Malaysia is capable of stopping the COVID-19 infectious virus. Virtual awareness programs should be conducted to provide the public with the most up-to-date information on infection control procedures and how to maintain a hygienic environment, as well as encourage people to adopt social distance and avoid social gatherings.

Projecting future infant mortality rate (IMR) is an important subject in ensuring the stability of health in one nation or a specific region in general. Secondary data of IMR from December 1950 until December 2020 from United NationsWorld Population Prospects were used to project the trend of IMR in Malaysia up to 2023. In this study, five different forecasting models were adopted including Mean model, Naïve model, Autoregressive Integrated Moving Average (ARIMA) model, Exponential State Space model and Neural Network model. The results were analyzed using R programing and RStudio. The out-sample forecasts of mortality rates were evaluated using six error measures namely, Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE). Consequently, the keen analysis was focused on the trend and projection of infant mortality rate in the future using the most accurate model. The results showed that the “win” model for this study is ARIMA (0,2,0) model. The model provided a consistent estimate of IMR in relation to a similar decreasing pattern as shown by the original data and hence a reliable projection of IMR. The three ahead forecast values showed that IMR is likely to keep on continuously decreasing in the future. This study could become a guideline for human resource management and health care allocation planning. A forecast of IMR can help the implementation of interventions to reduce the burden of infant mortality within the target range.