ABSTRACT
Vaccination has been used as strategy to eradicate the spread of COVID-19. But imperfect vaccine has been reported to induce backward bifurcation and hysteresis in mathematical models of disease transmission. Backward bifurcation is a phenomenon whereby a stable endemic equilibrium exists contemporaneously with a stable disease-free equilibrium when the basic reproduction number is less than 1. This situation can cause difficulty in controlling an epidemic because the basic reproduction is no longer the only means of eradicating the disease. In this paper, we propose a mathematical model for the transmission of disease which includes imperfect vaccination. We show that our model is capable of capturing backward bifurcation under certain conditions. By using parameters that are relevant to COVID-19 transmission in Malaysia, our numerical analysis shows that low vaccine efficacy can trigger backward bifurcation.
ABSTRACT
Radical changes across almost all areas, including education, due to the COVID-19 outbreak. One of the rapid transformations is digital learning, also known as e-learning. Digital learning transformation has been taking place for more than a decade. However, little comprehensive analysis of digital transformation in teaching in public schools. To the best of the authors' knowledge, no comprehensive analysis incorporates external and internal barriers and examines the prominent theories to study successful e-learning integration among teachers. The aim of this study was to provide a thematic and theoretical understanding of digital learning transformation among teachers in public schools. The data for the study was acquired from the Scopus databases. The study employed content and comparative analysis and advocated a grounded theory approach to inductively analyze and criticize the theme construction for answering two research questions. Based on a set of criteria to determine whether each derived study should be included or excluded, 42 articles were reviewed between 2010 and 2022. The analysis uncovered 10 themes of antecedents that were constructed as a framework based on the first-second-order barriers. Results also indicated that CHAT, TPACK, TAM, and UTAUT are the most prominent theories used to conduct digital transformation research. The findings offered significant implications for digital transformation and educational technology communities, especially for policymakers to strategize and reflect on the practice they implemented and improvised if necessary for future sustainable education and efficient teachers' performance in teaching. © Institute of Advanced Engineering and Science.
ABSTRACT
DNA aptamers are short nucleotides with a high affinity for their target. However, the process of isolating aptamers via the systematic evolution of ligands by exponential enrichment (SELEX) procedure is laborious. Therefore, an in silico approach is used to screen potential DNA aptamer candidates as a kickstart specifically for ORF7a of SARSCOV-2. By applying the TM-align program, the murine receptor (CD200R) protein was found to have structural similarities with ORF7a. Based on the literature, this CD200R protein is successfully bound by M49 DNA aptamers experimentally. Herein, the 3D structure of the M49 DNA aptamer was generated using Mfold, RNA Composer webserver, Discovery Studio Visualizer, and UCSF Chimera software, and the docking simulation was predicted using the HDOCK webserver. The binding energy scores for the M49-CD200R complex were slightly higher than those for the M49-ORF7a complex with-233.78 and-220.11, respectively. The molecular interaction in the complexes was contributed by the hydrogen bond. In conclusion, the M49 aptamer of CD200R protein can bind to the other similar target, the ORF7a protein of SARS-COV-2. Even though CD200R and ORF7a proteins share structural similarities, the binding sites of the individual complex are distinct. The current study shows that two different proteins with structural similarities may have a possibility to share the same DNA aptamer. This strategy may result in efficient aptamer discovery using an in silico method as a first step. © 2022 Marmara University Press.
ABSTRACT
Digital health technologies are being increasingly developed with the aim of allowing older adults to maintain functional independence throughout the old age, a process known as healthy ageing. Such digital health technologies for healthy ageing are expected to mitigate the pandemic effects and improve the quality of life of older adults. However, digital health technology diffusion is still in its early stage and there is slow rate of adoption among elderly people. Little is known regarding the needs of older adults regarding digital health technologies during pandemic. More reviews are needed to investigate on this issue so that engagement strategies for a better-quality digital health solution can be develop for promoting healthy ageing later. Therefore, the aim of this paper is to review on the perceptions and digital health technologies usage among older adults for healthy ageing during the COVID-19 epidemic. Summary of digital health technologies issues and recommended strategies for older adults is also presented. Related studies from the articles will be reviewed to highlights potential rooms for improvement for digital health technologies usage within older adults. © 2022 IJETAE Publication House. All rights reserved.
ABSTRACT
COVID-19 is a global public health problem that causes severe acute respiratory syndrome (SARS). It is also extremely contagious with rapidly increasing death rates. In this paper, we propose an optimal control model with SIRS (Susceptible-Infected-Recovered-Susceptible) kinetics to examine the effects of several intervention measures (e.g., vaccination and treatment) under the limited medical resources scenarios. This model is also employed to investigate the possibility of reinfection because of the fading of immunity problem. As a case study, the modeling framework is parametrised using COVID-19 daily confirmed and recovered cases in Malaysia. The parameters have been approximated by relying on the model's best fit to actual data published by the Malaysian Ministry of Health (MOH). Our numerical simulation results show that the inclusion of optimal control components with vaccination and treatment strategies would dramatically reduce the number of active cases even in the presence of reinfection forces. Regardless of the relative weightage (or costs) of vaccination and treatment, as well as the possibility of reinfection, it is critical to plan effective COVID-19 control measures by vaccinating as many people as possible (and as early as possible). Overall, these insights help explore the importance of intervention measures and the allocation of medical resources to control the severity of this pandemic.
ABSTRACT
Most institutions and organizations nowadays have been taking responsibility in reducing their carbon footprint (CF) to curtail the global warming impact to at least 20–25% reduction by 2030. Universities and higher learning institutions are starting to invest in becoming greener and carbon-free. Current COVID19 communicable disease has swayed the routine and concurrently influenced regular trends of greenhouse gases (GHG) emissions throughout the world. This study explored the possible GHG emissions (calculated as CO2e) from internal campus commute and purchased electricity consumption from the year 2018–2020 at Universiti Malaysia Perlis main campus to analyze the influence of COVID19 pandemic on its CO2e emission. The average amount of CO2e emitted during pre-COVID19 period (n = 26) was 1,518.8 tCO2e/year while during COVID19 period, it was 1,071.5 tCO2e/year (n = 10), marked as 29.5% reduction. Due to completeness and quality of data for contracted bus (monitoring period of years 2018, 2019 and 2020 as 12 months, 12 months, and 2 months, respectively), year 2019 was determined as the appropriate baseline year for setting the CO2e reduction target due to COVID19 pandemic precedented year. In comparison to pre-COVID19 pandemic, almost 95%/year and 7%/year reductions of CO2e were recorded for both Scope 1 and Scope 2, respectively. Comparing Scope 1 and 2, it was obviously observed that the purchased electricity consumption (Scope 2) was the predominant contributor to GHG emission at UniMAP campus by 78% despite of current pandemic influence and its reduction was indistinct (7%/year reduction). Thus, the reduction target in future should be venturing in energy savings and energy auditing in addition to carbon offsetting. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The Singular Spectrum Analysis (SSA) is a powerful non-parametric time series analysis that has demonstrated its capability in forecasting different time series in various disciplines. SSA falls in the framework of data-driven modelling of dynamical system which does not rely on any underlying assumption except the inherent dynamics which are captured over time. The capabilities of SSA are mainly aff orded by its direct connection to the singular value decomposition (SVD). It is generally accepted that SVDbased methods are very affective for the noise reduction in deterministic time series and consequently for forecasting, as well as for extracting trends and structures. Despite its strength, several shortcomings of SSA in the analysis of COVID-19 time series have been reported in the literature. The aim of this paper is to determine the scope of this limitation and we confine our investigation in the analysis and forecasting of COVID-19 Pandemic in Malaysia. We scrutinize the results fromthe SSA analysis of the number of daily confirmed cases to gain further insight into the intrinsic trends of the pandemic. Groupings of the singular spectra that contributes to different features of the pandemic time series are identified using analysis of the singular value spectrum, periodogram analysis and analysis of the weighted correlation matrix. It was revealed that under stationary conditions, the principal eigentriple is sufficient to produce reliable forecast. However, in non-stationary conditions, for example during a movement control order, it is useful to also study the minor eigentriples which could contain transient dynamics that may persist.
ABSTRACT
COVID-19 is a major health threat across the globe, which causes severe acute respiratory syndrome, and it is highly contagious with significant morbidity and mortality. In this paper, we examine the feasibility and implications of several phases of Movement Control Order (MCO) and some non-pharmaceutical intervention (NPI) strategies implemented by Malaysian government in the year 2020 using a mathematical model with SIR-neural network approaches. It is observed that this model is able to mimic the trend of infection trajectories of COVID-19 pandemic and, Malaysia had succeeded to flatten the infection curve at the end of the Conditional MCO (CMCO) period. However, the signs of 'flattening' with R-0 of less than one had been taken as a signal to ease up on some restrictions enforced before. Though the government has made compulsory the use of face masks in public places to control the spread of COVID-19, we observe a contrasting finding from our model with regards to the impacts of wearing mask policies in Malaysia on R0 and the infection curve. Additionally, other events such as the Sabah State Election at the end of third quarter of 2020 has also imposed a dramatic COVID-19 burden on the society and the healthcare systems.
ABSTRACT
During the current Covid-19 pandemic, the social relationship between humans has changed. As noticed, lockdowns and social distancing have become new norms. Unavoidably, counselling services were also affected with social distancing rules, especially in the school setting. Thus, online counselling or e-counselling has begun to gain in popularity, allowing counsellors to offer their services in an alternative way. In order to evaluate the suitability of e-counselling during the current pandemic, a client's satisfaction towards e-counselling and face-to-face counselling needs to be investigated. This study aims to explore the effectiveness of these two counselling approaches using the level of the client's satisfaction and its implication towards counselling practises during the outbreak. Quantitative data were obtained using the Client's Satisfaction Inventory Short-Form (CSI-SF) questionnaire. The experimental design consisted of a total of 60 study subjects in two groups, namely the control group using the face-to-face Counselling method and the experimental group using the e-Counselling method. Based on the results, the difference in mean score showed that the mean difference in the experimental group was slightly higher than the control group. This finding indicates that e-counselling clients have a higher satisfaction compared to the face-to-face approach. The implications of this data are discussed on the role of school, school counsellor, and the government in enhancing the service of e-counselling during the current pandemic.