Introduction: Depression is a common mental health disorder and afects many adolescents worldwide. Depression literacy can improve mental health outcomes. The aim of this study was to collate and analyse the extant evidence on depression literacy among adolescents, with particular focus on tools used to examine depression literacy and the indings on components of depression literacy. Methods: Nine electronic databases and 1 grey literature source were searched for studies published in English between January 2006 and December 2018 and involving adolescents aged 10-19 years. We included studies that reported on components of depression literacy such as knowledge, help-seeking and stigmatising attitudes. We excluded qualitative studies. Two independent reviewers veriied that the studies met the inclusion criteria, assessed the quality of the studies and extracted their characteristics. The data were descriptively analysed and appraised using the Newcastle-Ottawa Scale (NOS), Cochrane Collaboration's tool and the Quality Assessment Tool for Quantitative Studies (QATSQ). Results and conclusion: Fifty of the 14,626 references identiied met the inclusion criteria. Depression literacy was most commonly (58%) assessed using tools that utilize a vignette-based methodology. A lack of uniformity in reporting of depression literacy was noted. Adolescents were poor at recognising depression, likely to seek help from informal sources and tended to attach stigma to depression. The implications of the indings are discussed and suggestions made for future research.

Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, infectious, and recovered (SEIR) model by incorporating isolation, quarantine, and movement control order (MCO) taken in Malaysia. The simulations were fitted into the Malaysian COVID-19 active case numbers, allowing approximation of parameters consisting of probability of transmission per contact (β), average number of contacts per day per case (ζ), and proportion of close-contact traced per day (q). The effective reproduction number (Rt) was also determined through this model. Our model calibration estimated that (β), (ζ), and (q) were 0.052, 25 persons, and 0.23, respectively. The (Rt) was estimated to be 1.68. MCO measures reduce the peak number of active COVID-19 cases by 99.1% and reduce (ζ) from 25 (pre-MCO) to 7 (during MCO). The flattening of the epidemic curve was also observed with the implementation of these control measures. We conclude that isolation, quarantine, and MCO measures are essential to break the transmission of COVID-19 in Malaysia.

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.