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, βt 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

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

BackgroundGlobally, 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.MethodsThis 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.ResultsDengue 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.ConclusionThere 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

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 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

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

The Effect of Movement Control Order for Various Population Mobility Phases during COVID-19 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

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

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