Utilizing disease transmission and response capacities to optimize covid-19 control in Malaysia.

OBJECTIVES: Public Health Social Measures (PHSM) such as movement restriction movement needed to be adjusted accordingly during the COVID-19 pandemic to ensure low disease transmission alongside adequate health system capacities based on the COVID-19 situational matrix proposed by the World Health Organization (WHO). This paper aims to develop a mechanism to determine the COVID-19 situational matrix to adjust movement restriction intensity for the control of COVID-19 in Malaysia. METHODS: Several epidemiological indicators were selected based on the WHO PHSM interim guidance report and validated individually and in several combinations to estimate the community transmission level (CT) and health system response capacity (RC) variables. Correlation analysis between CT and RC with COVID-19 cases was performed to determine the most appropriate CT and RC variables. Subsequently, the CT and RC variables were combined to form a composite COVID-19 situational matrix (SL). The SL matrix was validated using correlation analysis with COVID-19 case trends. Subsequently, an automated web-based system that generated daily CT, RC, and SL was developed. RESULTS: CT and RC variables were estimated using case incidence and hospitalization rate; Hospital bed capacity and COVID-19 ICU occupancy respectively. The estimated CT and RC were strongly correlated [ρ = 0.806 (95% CI 0.752, 0.848); and ρ = 0.814 (95% CI 0.778, 0.839), p < 0.001] with the COVID-19 cases. The estimated SL was strongly correlated with COVID-19 cases (ρ = 0.845, p < 0.001) and responded well to the various COVID-19 case trends during the pandemic. SL changes occurred earlier during the increase of cases but slower during the decrease, indicating a conservative response. The automated web-based system developed produced daily real-time CT, RC, and SL for the COVID-19 pandemic. CONCLUSIONS: The indicators selected and combinations formed were able to generate validated daily CT and RC levels for Malaysia. Subsequently, the CT and RC levels were able to provide accurate and sensitive information for the estimation of SL which provided valuable evidence on the progression of the pandemic and movement restriction adjustment for the control of Malaysia.

Description of the COVID-19 epidemiology in Malaysia.

Introduction: Since the COVID-19 pandemic began, it has spread rapidly across the world and has resulted in recurrent outbreaks. This study aims to describe the COVID-19 epidemiology in terms of COVID-19 cases, deaths, ICU admissions, ventilator requirements, testing, incidence rate, death rate, case fatality rate (CFR) and test positivity rate for each outbreak from the beginning of the pandemic in 2020 till endemicity of COVID-19 in 2022 in Malaysia. Methods: Data was sourced from the GitHub repository and the Ministry of Health's official COVID-19 website. The study period was from the beginning of the outbreak in Malaysia, which began during Epidemiological Week (Ep Wk) 4 in 2020, to the last Ep Wk 18 in 2022. Data were aggregated by Ep Wk and analyzed in terms of COVID-19 cases, deaths, ICU admissions, ventilator requirements, testing, incidence rate, death rate, case fatality rate (CFR) and test positivity rate by years (2020 and 2022) and for each outbreak of COVID-19. Results: A total of 4,456,736 cases, 35,579 deaths and 58,906,954 COVID-19 tests were reported for the period from 2020 to 2022. The COVID-19 incidence rate, death rate, CFR and test positivity rate were reported at 1.085 and 0.009 per 1,000 populations, 0.80 and 7.57%, respectively, for the period from 2020 to 2022. Higher cases, deaths, testing, incidence/death rate, CFR and test positivity rates were reported in 2021 and during the Delta outbreak. This is evident by the highest number of COVID-19 cases, ICU admissions, ventilatory requirements and deaths observed during the Delta outbreak. Conclusion: The Delta outbreak was the most severe compared to other outbreaks in Malaysia's study period. In addition, this study provides evidence that outbreaks of COVID-19, which are caused by highly virulent and transmissible variants, tend to be more severe and devastating if these outbreaks are not controlled early on. Therefore, close monitoring of key epidemiological indicators, as reported in this study, is essential in the control and management of future COVID-19 outbreaks in Malaysia.

The seroprevalence of SARS-CoV-2 infection in Malaysia: 7 August to 11 October 2020.

Background: From the beginning of the COVID-19 pandemic until mid-October 2020, Malaysia recorded ~15,000 confirmed cases. But there could be undiagnosed cases due mainly to asymptomatic infections. Seroprevalence studies can better quantify underlying infection from SARS-CoV-2 by identifying humoral antibodies against the virus. This study was the first to determine the prevalence of SARS-CoV-2 infection in  Malaysia's general population, as well as the proportion of asymptomatic and undiagnosed infections. Methods: This cross-sectional seroprevalence study with a two-stage stratified random cluster sampling design included 5,131 representative community dwellers in Malaysia aged ≥1 year. Data collection lasted from 7 August to 11 October 2020 involving venous blood sampling and interviews for history of COVID-19 symptoms and diagnosis. Previous SARS-CoV-2 infection was defined as screened positive using the Wantai SARS-CoV-2 Total Antibody enzyme-linked immunosorbent assay and confirmed positive using the GenScript SARS-CoV-2 surrogate Virus Neutralization Test. We performed a complex sampling design analysis, calculating sample weights considering probabilities of selection, non-response rate and post-stratification weight. Results: The overall weighted prevalence of SARS-CoV-2 infection was 0.49% (95%CI 0.28-0.85) (N = 150,857). Among the estimated population with past infection, around 84.1% (95%CI 58.84-95.12) (N = 126 826) were asymptomatic, and 90.1% (95%CI 67.06-97.58) (N = 135 866) were undiagnosed. Conclusions: Our study revealed a low pre-variant and pre-vaccination seroprevalence of SARS-CoV-2 infection in Malaysia up to mid-October 2020, with a considerable proportion of asymptomatic and undiagnosed cases. This led to subsequent adoption of SARS-CoV-2 antigen rapid test kits to increase case detection rate and to reduce time to results and infection control measures.

Forecasting the effects of vaccination on the COVID-19 pandemic in Malaysia using SEIRV compartmental models.

OBJECTIVES: This study aimed to develop susceptible-exposed-infectious-recovered-vaccinated (SEIRV) models to examine the effects of vaccination on coronavirus disease 2019 (COVID-19) case trends in Malaysia during Phase 3 of the National COVID-19 Immunization Program amidst the Delta outbreak. METHODS: SEIRV models were developed and validated using COVID-19 case and vaccination data from the Ministry of Health, Malaysia, from June 21, 2021 to July 21, 2021 to generate forecasts of COVID-19 cases from July 22, 2021 to December 31, 2021. Three scenarios were examined to measure the effects of vaccination on COVID-19 case trends. Scenarios 1 and 2 represented the trends taking into account the earliest and latest possible times of achieving full vaccination for 80% of the adult population by October 31, 2021 and December 31, 2021, respectively. Scenario 3 described a scenario without vaccination for comparison. RESULTS: In scenario 1, forecasted cases peaked on August 28, 2021, which was close to the peak of observed cases on August 26, 2021. The observed peak was 20.27% higher than in scenario 1 and 10.37% lower than in scenario 2. The cumulative observed cases from July 22, 2021 to December 31, 2021 were 13.29% higher than in scenario 1 and 55.19% lower than in scenario 2. The daily COVID-19 case trends closely mirrored the forecast of COVID-19 cases in scenario 1 (best-case scenario). CONCLUSIONS: Our study demonstrated that COVID-19 vaccination reduced COVID-19 case trends during the Delta outbreak. The compartmental models developed assisted in the management and control of the COVID-19 pandemic in Malaysia.

The effects of the COVID-19 pandemic on dengue cases in Malaysia.

Background: Globally, the COVID-19 pandemic has affected the transmission dynamics and distribution of dengue. Therefore, this study aims to describe the impact of the COVID-19 pandemic on the geographic and demographic distribution of dengue incidence in Malaysia. Methods: This study analyzed dengue cases from January 2014 to December 2021 and COVID-19 confirmed cases from January 2020 to December 2021 which was divided into the pre (2014 to 2019) and during COVID-19 pandemic (2020 to 2021) phases. The average annual dengue case incidence for geographical and demographic subgroups were calculated and compared between the pre and during the COVID-19 pandemic phases. In addition, Spearman rank correlation was performed to determine the correlation between weekly dengue and COVID-19 cases during the COVID-19 pandemic phase. Results: Dengue trends in Malaysia showed a 4-year cyclical trend with dengue case incidence peaking in 2015 and 2019 and subsequently decreasing in the following years. Reductions of 44.0% in average dengue cases during the COVID-19 pandemic compared to the pre-pandemic phase was observed at the national level. Higher dengue cases were reported among males, individuals aged 20-34 years, and Malaysians across both phases. Weekly dengue cases were significantly correlated (ρ = -0.901) with COVID-19 cases during the COVID-19 pandemic. Conclusion: There was a reduction in dengue incidence during the COVID-19 pandemic compared to the pre-pandemic phase. Significant reductions were observed across all demographic groups except for the older population (>75 years) across the two phases.

Resurgence of Dengue Virus Serotype 4 in Malaysia: A Comprehensive Clinicodemographic and Genomic Analysis.

Dengue virus serotype 4 (DENV-4) has been the rarest circulating serotype in Malaysia, resulting in it being an understudied area. A recent observation from institutional surveillance data indicated a rapid increase in DENV-4-infected cases. The present study aimed to investigate the resurgence of DENV-4 in relation to the demographic, clinical and genomic profiles of 75 retrospective dengue samples. First, the demographic and clinical profiles obtained between 2017 and July 2022 were statistically assessed. Samples with good quality were subjected to full genome sequencing on the Illumina Next Seq 500 platform and the genome data were analysed for the presence of mutations. The effect of the mutations of interest was studied via an in silico computational approach using SWISS-MODEL and AlphaFold2 programs. The predominance of DENV-4 was discovered from 2021 to 2022, with a prevalence of 64.3% (n = 9/14) and 89.2% (n = 33/37), respectively. Two clades with a genetic divergence of 2.8% were observed within the dominant genotype IIa. The majority of DENV-4-infected patients presented with gastrointestinal symptoms, such as vomiting (46.7%), persistent diarrhoea (30.7%) and abdominal pain (13.3%). Two mutations, His50Tyr and Pro144Ser, located at the wing domain of the NS1 protein were discovered to be unique to the recently sequenced DENV-4.

Prevalence and Determinants of Depressive Symptoms among Young Adolescents in Malaysia: A Cross-Sectional Study.

Depression is the most common mental health problem affecting adolescents globally, wherein its increasing prevalence together with the negative health impacts escalates the need for further research in this area. This work determined the prevalence and factors associated with depressive symptoms among young adolescents in Malaysia. A total of 1350 adolescent aged 13 to 14 years in school across nine secondary schools in Selangor state, Malaysia participated in a cross-sectional study. Independent variables were examined using the using the Global School-Based Student Health Survey included age, gender, ethnicity, alcohol intake, smoking and illicit drug use, loneliness, bullying, parental marital status, income and supervision; and the Health Literacy and Stigma questionnaire examined mental health literacy levels. Depressive symptoms were the dependent variable which was examined using the Center for Epidemiology Study Depression (CESD) instrument. Prevalence of depressive symptoms among all participants was 19 % (95% CI [16.9, 21.2]), with a higher prevalence of depressive symptoms being reported among females 26.3% (95% CI [23.0, 29.8]) compared to males 11.7% (95% CI [9.4, 14.4]). Determinants namely females (AOR = 3.83; 95% CI [2.66, 5.52]), smoking (AOR = 6.16; 95% CI [3.15, 12.05]), been bullied (AOR = 3.70; 95% CI [2.51, 5.47]), felt lonely (AOR = 10.46; 95% CI [7.09, 15.42]) and having no parental supervision (AOR = 1.79; 95% CI [1.26, 2.53]) significantly increased the odds of depressive symptoms among all adolescents in the multivariate model. In addition, feeling lonely, being bullied and smoking were identified as common significant determinants of depressive symptoms across both genders. Feeling lonely (65% to 71%) and being bullied (10% to 19%) were ranked as the most important determinants of depressive symptoms among young adolescents. Tackling these factors would be instrumental in helping decision makers formulate depression prevention strategies and activities for adolescents.

Surveillance, isolation and genomic characterization of Pteropine orthoreovirus of probable bat origin among patients with acute respiratory infection in Malaysia.

Pteropine orthoreovirus (PRV), an emerging bat-borne virus, has been linked to cases of acute respiratory infections (ARI) in humans. The prevalence, epidemiology and genomic diversity of PRV among ARI of unknown origin were studied. Among 632 urban outpatients tested negative for all known respiratory viruses, 2.2% were PRV-positive. Patients mainly presented with moderate to severe forms of cough, sore throat and muscle ache, but rarely with fever. Phylogenetic analysis revealed that over 90% of patients infected with the Melaka virus (MelV)-like PRV, while one patient infected with the Pulau virus previously found only in fruit bats. Human oral keratinocytes and nasopharyngeal epithelial cells were susceptible to clinical isolates of PRV, including the newly isolated MelV-like 12MYKLU1034. Whole genome sequence of 12MYKLU1034 using Nanopore technique revealed a novel reassortant strain. Evolutionary analysis of the global PRV strains suggests the continuous evolution of PRV through genetic reassortment among PRV strains circulating in human, bats and non-human primate hosts, creating a spectrum of reassortant lineages with complex evolutionary characteristics. In summary, the role of PRV as a common etiologic agent of ARI is evident. Continuous monitoring of PRV prevalence, pathogenicity and diversity among human and animal hosts is important to trace the emergence of novel reassortants.

Development and user testing study of MozzHub: a bipartite network-based dengue hotspot detector.

Traditionally, dengue is controlled by fogging, and the prime location for the control measure is at the patient's residence. However, when Malaysia was hit by the first wave of the Coronavirus disease (COVID-19), and the government-imposed movement control order, dengue cases have decreased by more than 30% from the previous year. This implies that residential areas may not be the prime locations for dengue-infected mosquitoes. The existing early warning system was focused on temporal prediction wherein the lack of consideration for spatial component at the microlevel and human mobility were not considered. Thus, we developed MozzHub, which is a web-based application system based on the bipartite network-based dengue model that is focused on identifying the source of dengue infection at a small spatial level (400 m) by integrating human mobility and environmental predictors. The model was earlier developed and validated; therefore, this study presents the design and implementation of the MozzHub system and the results of a preliminary pilot test and user acceptance of MozzHub in six district health offices in Malaysia. It was found that the MozzHub system is well received by the sample of end-users as it was demonstrated as a useful (77.4%), easy-to-operate system (80.6%), and has achieved adequate client satisfaction for its use (74.2%).

Effectiveness of Ultrasound-guided versus Anatomical Landmark-guided Genicular Nerve Block to Treat Chronic Knee Osteoarthritis: A Retrospective Cohort Study.

Objectives: There is limited data on the relative effectiveness of different techniques used for administering genicular nerve block (GNB) for pain management of chronic knee osteoarthritis (OA) in the Malaysian population. This study aims to determine and compare the effectiveness of GNB administered using two pain management techniques?"anatomical landmark-guided (ALG) and ultrasound-guided (USG)?"for chronic knee OA in this population. Methods: This retrospective cohort study included 40 patients with chronic knee OA who received GNB, 20 of whom underwent treatment with the USG technique and the other 20 with the ALG technique. Pain, stiffness, and functional limitation scores were assessed using the Western Ontario and McMaster Universities Osteoarthritis Index Questionnaire (WOMAC) and Numeric Rating Scale (NRS-11) at baseline and post-treatment day one, three weeks, and six weeks. Results: Both groups reported a significant reduction in WOMAC and NRS-11 scores as per their feedback on day one, three weeks, and six weeks post-treatment. Greater reductions in WOMAC and NRS-11 scores were reported by patients who received GNB via USG than by ALG technique, the difference achieving statistical significance at six weeks after treatment (p =0.026). Conclusions: GNB administration using USG and ALG techniques are both effective in significantly reducing pain, stiffness, and functional limitation in patients suffering from chronic knee OA. Among the two techniques, USG appears to be more effective. Nevertheless, GNB guided by ALG continues to be a viable treatment modality, especially in healthcare settings with limited to no USG facilities.

Correlation between Population Density and COVID-19 Cases during the Third Wave in Malaysia: Effect of the Delta Variant.

In this study, we describe the incidence and distribution of COVID-19 cases in Malaysia at district level and determine their correlation with absolute population and population density, before and during the period that the Delta variant was dominant in Malaysia. METHODS: Data on the number of locally transmitted COVID-19 cases in each of the 145 districts in Malaysia, between 20 September 2020 and 19 September 2021, were manually extracted from official reports. The cumulative number of COVID-19 cases, population and population density of each district were described using choropleth maps. The correlation between population and population density with the cumulative number of COVID-19 cases in each district in the pre-Delta dominant period (20 September 2020-29 June 2021) and during the Delta dominant period (30 June 2021-19 September 2021) were determined using Pearson's correlation. RESULTS: COVID-19 cases were strongly correlated with both absolute population and population density (Pearson's correlation coefficient (r) = 0.87 and r = 0.78, respectively). A majority of the districts had higher numbers of COVID-19 cases during the Delta dominant period compared to the pre-Delta period. The correlation coefficient in the pre-Delta dominant period was r = 0.79 vs. r = 0.86 during the Delta dominant period, whereas the pre-Delta dominant population density was r = 0.72, and in the Delta dominant period, r = 0.76. CONCLUSION: More populous and densely populated districts have a higher risk of transmission of COVID-19, especially with the Delta variant as the dominant circulating strain. Therefore, extra and more stringent control measures should be instituted in highly populated areas to control the spread of COVID-19.

The Effects of Meteorological Factors on Dengue Cases in Malaysia.

Dengue is a vector-borne disease affected by meteorological factors and is commonly recorded from ground stations. Data from ground station have limited spatial representation and accuracy, which can be overcome using satellite-based Earth Observation (EO) recordings instead. EO-based meteorological recordings can help to provide a better understanding of the correlations between meteorological variables and dengue cases. This paper aimed to first validate the satellite-based (EO) data of temperature, wind speed, and rainfall using ground station data. Subsequently, we aimed to determine if the spatially matched EO data correlated with dengue fever cases from 2011 to 2019 in Malaysia. EO data were spatially matched with the data from four ground stations located at states and districts in the central (Selangor, Petaling) and east coast (Kelantan, Kota Baharu) geographical regions of Peninsular Malaysia. Spearman's rank-order correlation coefficient (ρ) was performed to examine the correlation between EO and ground station data. A cross-correlation analysis with an eight-week lag period was performed to examine the magnitude of correlation between EO data and dengue case across the three time periods (2011-2019, 2015-2019, 2011-2014). The highest correlation between the ground-based stations and corresponding EO data were reported for temperature (mean ρ = 0.779), followed by rainfall (mean ρ = 0.687) and wind speed (mean ρ = 0.639). Overall, positive correlations were observed between weekly dengue cases and rainfall for Selangor and Petaling across all time periods with significant correlations being observed for the period from 2011 to 2019 and 2015 to 2019. In addition, positive significant correlations were also observed between weekly dengue cases and temperature for Kelantan and Kota Baharu across all time periods, while negative significant correlations between weekly dengue cases and temperature were observed in Selangor and Petaling across all time periods. Overall negative correlations were observed between weekly dengue cases and wind speed in all areas from 2011 to 2019 and 2015 to 2019, with significant correlations being observed for the period from 2015 to 2019. EO-derived meteorological variables explained 48.2% of the variation in dengue cases in Selangor. Moderate to strong correlations were observed between meteorological variables recorded from EO data derived from satellites and ground stations, thereby justifying the use of EO data as a viable alternative to ground stations for recording meteorological variables. Both rainfall and temperature were found to be positively correlated with weekly dengue cases; however, wind speed was negatively correlated with dengue cases.

COVID-19 in Malaysia: Descriptive Epidemiologic Characteristics of the First Wave.

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 (R0) 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 R0 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.

The Determinants of Mental Health Literacy among Young Adolescents in Malaysia.

Mental health literacy (MHL) is an established multifaceted concept that comprises mental health knowledge, help-seeking, and stigma. Adequate MHL (i.e., the ability to correctly recognize mental health disorders alongside having the intention to seek help) is able improve mental health outcomes among individuals. This study aims to examine the determinants of MHL among young Malaysian adolescents. A cross-sectional study was conducted among 1400 adolescents between 13 and 14 years old from nine national secondary schools in Selangor state, Malaysia. Sociodemographic determinants assessed included gender, age, ethnicity, smoking status, alcohol consumption, history of being bullied, feeling lonely, parental marital status, and parental income which were assessed using the Global School Based Student Health Survey. MHL was assessed using the Mental Health Literacy and Stigma questionnaire. Several factors were significantly associated with adequate levels of MHL following multivariate analysis, such as being female (AOR = 1.68; 95% CI 1.12, 2.52), older adolescents (AOR = 1.56; 95% CI 1.07, 2.30), not smoking (AOR = 1.99; 95% CI 1.20, 4.26), not consuming alcohol (AOR = 1.23; 95% CI 1.18, 2.41), and not feeling lonely (AOR = 1.25; 95% CI 1.06, 1.85). Addressing these determinants could be key in assisting the development of policies and programs to prevent mental health disorders among adolescents, which are currently on the rise.

The effects of super spreading events and movement control measures on the COVID-19 pandemic in Malaysia.

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.

Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia.

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.

Effectiveness of the movement control measures during the third wave of COVID-19 in Malaysia.

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.

A data driven change-point epidemic model for assessing the impact of large gathering and subsequent movement control order on COVID-19 spread in Malaysia.

The second wave of COVID-19 in Malaysia is largely attributed to a four-day mass gathering held in Sri Petaling from February 27, 2020, which contributed to an exponential rise of COVID-19 cases in the country. Starting from March 18, 2020, the Malaysian government introduced four consecutive phases of a Movement Control Order (MCO) to stem the spread of COVID-19. The MCO was implemented through various non-pharmaceutical interventions (NPIs). The reported number of cases reached its peak by the first week of April and then started to reduce, hence proving the effectiveness of the MCO. To gain a quantitative understanding of the effect of MCO on the dynamics of COVID-19, this paper develops a class of mathematical models to capture the disease spread before and after MCO implementation in Malaysia. A heterogeneous variant of the Susceptible-Exposed-Infected-Recovered (SEIR) model is developed with additional compartments for asymptomatic transmission. Further, a change-point is incorporated to model disease dynamics before and after intervention which is inferred based on data. Related statistical analyses for inference are developed in a Bayesian framework and are able to provide quantitative assessments of (1) the impact of the Sri Petaling gathering, and (2) the extent of decreasing transmission during the MCO period. The analysis here also quantitatively demonstrates how quickly transmission rates fall under effective NPI implementation within a short time period. The models and methodology used provided important insights into the nature of local transmissions to decision makers in the Ministry of Health, Malaysia.

Reliability analysis of the Malay version of the center for epidemiologic studies-depression scale (CESD) among adolescents in Malaysia.

Depression is a common mental disorder that affects many adolescents worldwide. Therefore, there is a need for reliable instruments to screen for depression symptoms among adolescents. This study aims to determine the reliability of the Malay version of the Centre of Epidemiological Studies Depression Scale (CESD) among adolescents in Malaysia. A cross-sectional study was conducted among 65 adolescents ages between 12 and 14 years from two secondary schools in the Federal Territory of Kuala Lumpur from May 2017 to July 2017. Cronbach's alpha (α), McDonald's omega (ω), Spearman Brown split half reliability (rSB), and Intra-class Correlation Coefficient (ICC) were examine to determine the internal consistency and two week test-retest reliability. The overall CESD scale was found to have good internal consistency with α = 0.882 (95% CI 0.837, 0.914), ω = 0.886 (95% CI 0.837, 0.916) and rSB = 0.909. The CESD subscales, Somatic symptoms (α = 0.824; 95% CI 0.739, 0.878; ω = 0.828; 95% CI 0.738, 0.885; rSB = 0.825), Depressive affect (α = 0.822; 95% CI 0.745, 0.880; ω = 0.834; 95% CI 0.750, 0.884; rSB = 0.847) and Positive affect (α = 0.610; 95% CI 0.326, 0.721; ω = 0.612; 95% CI 0.379, 0.723 and rSB = 0.608) indicated acceptable to good internal consistency. The 2-week test-retest reliability ICC was 0.926 (95% CI 0.851, 0.961) for the total score reliability. The reliability analysis of the Malay version of CESD shows satisfactory α, ω, rSB and ICC values, therefore making it a reliable instrument to screen for depression among adolescents in Malaysia.

Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model.

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, [Formula: see text] 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.