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This paper aims to develop an automated web application to generate validated daily effective reproduction numbers (Rt) which can be used to examine the effects of super-spreading events due to mass gatherings and the effectiveness of the various Movement Control Order (MCO) stringency levels on the outbreak progression of COVID-19 in Malaysia. The effective reproduction number, Rt, was estimated by adopting and modifying an Rt estimation algorithm using a validated distribution mean of 3.96 and standard deviation of 4.75 with a seven-day sliding window. The Rt values generated were validated using thea moving window SEIR model with a negative binomial likelihood fitted using methods from the Bayesian inferential framework. A Pearson's correlation between the Rt values estimated by the algorithm and the SEIR model was r = 0.70, p < 0.001 and r = 0.81, p < 0.001 during the validation period The Rt increased to reach the highest values at 3.40 (95% CI 1.47, 6.14) and 1.72 (95% CI 1.54, 1.90) due to the Sri Petaling and Sabah electoral process during the second and third waves of COVID-19 respectively. The MCOs was able to reduce the Rt values by 63.2 to 77.1% and 37.0 to 47.0% during the second and third waves of COVID-19, respectively. Mass gathering events were one of the important drivers of the COVID-19 outbreak in Malaysia. However, COVID-19 transmission can be fuelled by noncompliance to Standard Operating Procedure, population mobility, ventilation and environmental factors. [ABSTRACT FROM AUTHOR]

The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I) and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, β t and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4.7% each day with a decreased capacity of 40%. For 7-day and 14-day projections, the modified SIR model accurately predicted I total, I and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: In 2008-2010, Malaysia experienced a nationwide chikungunya virus (CHIKV) outbreak caused by the Indian Ocean lineage E1-226V (valine) variant, adapted to Aedes albopictus. In 2017-2022, transition to an E1-226A (alanine) variant occurred. Ae. albopictus prevails in rural areas, where most cases occurred during the E1-226V outbreak, while Ae. aegypti dominates urban areas. The shift in circulating CHIKV variants from E1-226V to E1-226A (2009-2022) was hypothesized to result in a transition from rural to urban CHIKV distribution, driven by differences in Ae. aegypti vector competence for the two variants. This study aimed to: (1) map the spatiotemporal spread of CHIKV cases in Malaysia between 2009-2022; and (2) compare replication of E1-226A and E1-226V variants in the midguts and head/thoraxes of Ae. aegypti., Methodology/principal Findings: Spatiotemporal analysis of national notified CHIKV case addresses was performed. Between 2009-2022, 12,446 CHIKV cases were reported, with peaks in 2009 and 2020, and a significant shift from predominantly rural cases in 2009-2011 (85.1% rural), to urban areas in 2017-2022 (86.1% urban; p<0.0001). Two Ae. aegypti strains, field-collected MC1 and laboratory Kuala Lumpur (KL) strains, were fed infectious blood containing constructed CHIKV clones, pCMV-p2020A (E1-226A) and pCMV-p2020V (E1-226V) to measure CHIKV replication by real-time PCR and/or virus titration. The pCMV-p2020A clone replicated better in Ae. aegypti cell line Aag2 and showed higher replication, infection and dissemination efficiency in both Ae. aegypti strains, compared to pCMV-p2020V., Conclusions/significance: This study revealed that a change in circulating CHIKV variants can be associated with changes in vector competence and outbreak epidemiology. Continued genomic surveillance of arboviruses is important., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: YFC currently serves as a member of the editorial board of PLoS Neglected Tropical Diseases. This will not alter adherence to PLOS policies on sharing data and materials. All other authors have declared that no competing interests exist., (Copyright: © 2024 Azman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Cardiovascular disease (CVD) is a major health concern worldwide, particularly in low- and middle-income countries. The COVID-19 pandemic that emerged in late 2019 may have had an impact on the trend of CVD mortality. This study aimed to investigate the trend and changes in CVD mortality rates in Malaysia, using age-standardized mortality rates (ASMR) from 2010 to 2021., Methods: The Malaysian population and mortality data from 2010 to 2021 were obtained from the Department of Statistics Malaysia (DOSM). ASMRs from CVD per 100,000 population were calculated based on the World Health Organization (2000-2025) standard population using the direct method. The ASMRs were computed based on sex, age groups (including premature mortality age, 30-69 years), and CVD types. The annual percent change (APC) and average annual percent change (AAPC) of the ASMR with corresponding 95% confidence intervals (95% CI) were estimated from joinpoint regression model using the Joinpoint Regression Program, Version 4.9.1.0., Results: Throughout the study period (2010-2021), ASMRs for CVD exhibited an increase from 93.1 to 147.0 per 100,000, with an AAPC of 3.6% (95% CI: 2.1 to 5.2). The substantial increase was observed between 2015 and 2018 (APC 12.6%, 95% CI: 5.4%, 20.3%), with significant changes in both sexes, and age groups 50-69, 70 years and over, and 30-69 (premature mortality age). Notably, the ASMR trend remained consistently high in the premature mortality age group across other age groups, with males experiencing higher rates than females. No significant changes were detected before or after the COVID-19 pandemic (between 2019 and 2021), except for females who died from IHD (10.3% increase) and those aged 0-4 (25.2% decrease)., Conclusion: Overall, our analysis highlights the persistently high burden of CVD mortality in Malaysia, particularly among the premature mortality age group. These findings underscore the importance of continued efforts to address CVD risk factors and implement effective prevention and management strategies. Further research is needed to fully understand the impact of the COVID-19 pandemic on CVD mortality rates and to inform targeted interventions to reduce the burden of CVD in Malaysia., (© 2024. The Author(s).)

This study aimed to describe the characteristics of COVID-19 cases and close contacts during the first wave of COVID-19 in Malaysia (23 January 2020 to 26 February 2020), and to analyse the reasons why the outbreak did not continue to spread and lessons that can be learnt from this experience. Characteristics of the cases and close contacts, spatial spread, epidemiological link, and timeline of the cases were examined. An extended SEIR model was developed using several parameters such as the average number of contacts per day per case, the proportion of close contact traced per day and the mean daily rate at which infectious cases are isolated to determine the basic reproduction number (R 0 ) and trajectory of cases. During the first wave, a total of 22 cases with 368 close contacts were traced, identified, tested, quarantine and isolated. Due to the effective and robust outbreak control measures put in place such as early case detection, active screening, extensive contact tracing, testing and prompt isolation/quarantine, the outbreak was successfully contained and controlled. The SEIR model estimated the R 0 at 0.9 which further supports the decreasing disease dynamics and early termination of the outbreak. As a result, there was a 11-day gap (free of cases) between the first and second wave which indicates that the first wave was not linked to the second wave.

With many countries experiencing a resurgence in COVID-19 cases, it is important to forecast disease trends to enable effective planning and implementation of control measures. This study aims to develop Seasonal Autoregressive Integrated Moving Average (SARIMA) models using 593 data points and smoothened case and covariate time-series data to generate a 28-day forecast of COVID-19 case trends during the third wave in Malaysia. SARIMA models were developed using COVID-19 case data sourced from the Ministry of Health Malaysia's official website. Model training and validation was conducted from 22 January 2020 to 5 September 2021 using daily COVID-19 case data. The SARIMA model with the lowest root mean square error (RMSE), mean absolute percentage error (MAE) and Bayesian information criterion (BIC) was selected to generate forecasts from 6 September to 3 October 2021. The best SARIMA model with a RMSE = 73.374, MAE = 39.716 and BIC = 8.656 showed a downward trend of COVID-19 cases during the forecast period, wherein the observed daily cases were within the forecast range. The majority (89%) of the difference between the forecasted and observed values was well within a deviation range of 25%. Based on this work, we conclude that SARIMA models developed in this paper using 593 data points and smoothened data and sensitive covariates can generate accurate forecast of COVID-19 case trends.

The state of Selangor, in Malaysia consist of urban and peri-urban centres with good transportation system, and suitable temperature levels with high precipitations and humidity which make the state ideal for high number of dengue cases, annually. This study investigates if districts within the Selangor state do influence each other in determining pattern of dengue cases. Study compares two different models; the Autoregressive Integrated Moving Average (ARIMA) and Ensemble ARIMA models, using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) measurement to gauge their performance tools. ARIMA model is developed using the epidemiological data of dengue cases, whereas ensemble ARIMA incorporates the neighbouring regions' dengue models as the exogenous variable (X), into traditional ARIMA model. Ensemble ARIMA models have better model fit compared to the basic ARIMA models by incorporating neighbuoring effects of seven districts which made of state of Selangor. The AIC and BIC values of ensemble ARIMA models to be smaller compared to traditional ARIMA counterpart models. Thus, study concludes that pattern of dengue cases for a district is subject to spatial effects of its neighbouring districts and number of dengue cases in the surrounding areas.

Objectives: Starting in March 2020, movement control measures were instituted across several phases in Malaysia to break the chain of transmission of coronavirus disease 2019 (COVID-19). In this study, we developed a susceptible-exposed-infected-recovered (SEIR) model to examine the effects of the various phases of movement control measures on disease transmissibility and the trend of cases during the third wave of the COVID-19 pandemic in Malaysia., Methods: Three SEIR models were developed using the R programming software ODIN interface based on COVID-19 case data from September 1, 2020, to March 29, 2021. The models were validated and subsequently used to provide forecasts of daily cases from October 14, 2020, to March 29, 2021, based on 3 phases of movement control measures., Results: We found that the reproduction rate (R-value) of COVID-19 decreased by 59.1% from an initial high of 2.2 during the nationwide Recovery Movement Control Order (RMCO) to 0.9 during the Movement Control Order (MCO) and Conditional MCO (CMCO) phases. In addition, the observed cumulative and daily highest numbers of cases were much lower than the forecasted cumulative and daily highest numbers of cases (by 64.4-98.9% and 68.8-99.8%, respectively)., Conclusions: The movement control measures progressively reduced the R-value during the COVID-19 pandemic. In addition, more stringent movement control measures such as the MCO and CMCO were effective for further lowering the R-value and case numbers during the third wave of the COVID-19 pandemic in Malaysia due to their higher stringency than the nationwide RMCO.