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Purpose: The coronavirus disease 2019 (COVID-19) has a significant influence on many aspects of life, including education. As a result, the education system in emerging nations such as Bangladesh needs a rapid transition from conventional to technology-based distance learning. This study looks at the current state of higher education and how well online courses that use technology work. Design/methodology/approach: This study used a structural equation model (SEM) to analyze the 392 student records taken from several universities in Bangladesh. Findings: This research showed that students are more likely to use a digital higher education system if faculty are willing, students are eager and the economy is stable. Students who have had a bad experience with digital learning should know that a virtual evaluation system is needed. The willingness of students to use technology also plays a significant role in whether or not the students will take online classes. The research shows that combining traditional classroom and online learning is the best way to create a long-term learning system. Originality/value: The model suggested in this study has a big effect, and Bangladesh policymakers should consider this model when planning a new kind of technology-based education. © 2023, Emerald Publishing Limited.
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The vaccination program which helps avert pandemics is facing new hurdles, including the emergence of hazardous new virus strains and public distrust. Analyzing the sentiment expressed in social media interactions related to vaccines may aid the health authority in implementing public safety procedures and guide the government in developing appropriate policies. The purpose of this research is to identify the public sentiments toward the COVID-19 vaccination in Bangladesh from social media comments. Comments posted on social media platforms often mix formal and informal language known as code-mixed text and do not adhere to any particular grammatical standards. In addition, the Bangla language lacks computational models and annotated resources for sentiment analysis. To overcome this, we created CoVaxBD, a Bangla-English code-mixed and sentiment-annotated corpus of Facebook comments. This paper also proposes a model for sentiment analysis based on the multilingual BERT. It achieves a validation accuracy of around 97.3 % and a precision score of approximately 97.4%. © 2022 IEEE.
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The death t toll of the coronavirus disease 2019 (COVID-19) has been considerable. Several risk factors have been linked to mortality due to COVID-19 in hospitals. This study aimed to describe the clinical characteristics of patients who either died from COVID-19 at Dhaka Medical College Hospital in Bangladesh. In this retrospective study, we reviewed the hospital records of patients who died or recovered and tested positive for COVID-19 from May 3 to August 31, 2020. All patients who died during the study period were included in the analysis. A comparison group of patients who survived COVID-19 at the same hospital during the same period was systematically sampled. All available information was retrieved from the records, including demographic, clinical, and laboratory variables. Of the 3115 patients with confirmed COVID-19 during the study period, 282 died.The mean age of patients who died was higher than that of those who survived (56.7 vs 52.6 years). Approximately three-fourths of deceased patients were male. History of smoking (risk ratio 2.3;95% confidence interval: 1.6-3.4), comorbidities (risk ratio: 1.5;95% confidence interal:1.1-2.1), chronic kidney disease (risk ratio: 3.2;95% confidence interval: 1.7-6.25), and ischemic heart disease (risk ratio:1.8;95% confidence interval: 1.1-2.9) were higher among the deceased than among those who survived. Mean C-reactive protein and D-dimer levels [mean (interquartile range), 34 (21-56) vs. 24 (12-48);and D-dimer [1.43 (1-2.4) vs. 0.8 (0.44-1.55)] were higher among those who died than among those who recovered. Older age, male sex, rural residence, history of smoking, and chronic kidney disease were found to be important predictors of mortality. Early hospitalization should be considered for patients with COVID-19 who are older, male, and have chronic kidney disease. Rapid referral to tertiary care facilities is necessary for high-risk patients in rural settings.Copyright © 2023 Hoque MM.
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The COVID-19 pandemic has affected every country's health service and plunged refugees into the most desperate conditions. The plight of Rohingya refugees is among the harshest. COVID-19 has severely affected their existing HIV/STI prevention and management services and further increased the risk of violence and onward HIV transmission within the camps. In this commentary, we discuss the context and the changing dynamics of HIV/AIDS during COVID-19 pandemic, among the Rohingya refugee community in Bangladesh. What we currently observe is the worst crisis in the Rohingya refugee camps thus far. Because of being displaced, Rohingya refugees have increased vulnerability to HIV, STIs and other poor health outcomes. They have inadequate access to HIV testing, treatment, and care. Their host country has poor capacity to provide services. Complex economic, socio-cultural and behavioural factors exacerbate their poor access to HIV testing, treatment, and care. The unfolding COVID-19 pandemic has changed priorities in the Rohingya refugee camps so that more emphasis is being placed on COVID-19 prevention and treatment rather than other health issues. This exacerbates the already dire situation with HIV detection, management, and prevention among the refugees. Although the government of Bangladesh and different non-governmental organisations provide harm reduction, HIV care, and COVID-19 care to refugees, a comprehensive response is needed to maintain and strengthen health programs for refugees, for both HIV and COVID-19 care. This comprehensive response should include behavioural intervention, community mobilisation, and effective treatment and care. Without addressing the disadvantage of social conditions, it will be challenging to reduce the burden of HIV and COVID-19 among refugees. While the COVID-19 crisis is a global challenge, the international community has an obligation to improve the life, livelihood and health of those who are most vulnerable. Rohingya refugees are among them. © 2022 The Author(s).
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PurposeFake news on social media about COVID-19 pandemic and its associated issues (e.g. lockdown) caused public panic that lead to supply chain (SC) disruptions, which eventually affect firm performance. The purpose of this study is to understand how social media fake news effects firm performance, and how to mitigate such effects.Design/methodology/approachGrounded on dynamic capability view (DCV), this study suggests that social media fake news effects firm performance via SC disruption (SCD) and SC resilience (SCR). Moreover, the relation between SCD and SCR is contingent upon SC learning (SCL) - a moderated mediation effect. To validate this complex model, the authors suggest effectiveness of using partial least squares structural equation modeling (PLS-SEM). Using an online survey, the results support the authors' hypotheses.FindingsThe results suggest that social media fake news does not affect firm performance directly. However, the authors' serial mediation test confirms that SCD and SCR sequentially mediate the relationship between social media fake news and firm performance. In addition, a moderated serial mediation test confirms that a higher level of SCL strengthens the SCD-SCR relationship.Research limitations/implicationsThis work offers a new theoretical and managerial perspective to understand the effect of fake news on firm performance, in the context of crises, e.g. COVID-19. In addition, this study offers the advancement of PLS as more robust for real-world applications and more advantageous when models are complex.Originality/valuePrior studies in the SC and marketing domain suggest different effects of social media fake news on consumer behavior (e.g. panic buying) and SCD, respectively. This current study is a unique effort that investigates the ultimate effect of fake news on firm performance with complex causal relationships via SCD, SCR and SCL.
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Diagnostic testing to identify individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays a key role in selecting appropriate treatments, saving people's lives and preventing the global pandemic of COVID-19. By testing on a massive scale, some countries could successfully contain the disease spread. Since early viral detection may provide the best approach to curb the disease outbreak, the rapid and reliable detection of coronavirus (CoV) is therefore becoming increasingly important. Nucleic acid detection methods, especially real-time reverse transcription polymerase chain reaction (RT-PCR)-based assays are considered the gold standard for COVID-19 diagnostics. Some non-PCR-based molecular methods without thermocycler operation, such as isothermal nucleic acid amplification have been proved promising. Serologic immunoassays are also available. A variety of novel and improved methods based on biosensors, Clustered-Regularly Interspaced Short Palindromic Repeats (CRISPR) technology, lateral flow assay (LFA), microarray, aptamer etc. have also been developed. Several integrated, random-access, point-of-care (POC) molecular devices are rapidly emerging for quick and accurate detection of SARS-CoV-2 that can be used in the local hospitals and clinics. This review intends to summarize the currently available detection approaches of SARS-CoV-2, highlight gaps in existing diagnostic capacity, and propose potential solutions and thus may assist clinicians and researchers develop better technologies for rapid and authentic diagnosis of CoV infection.
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Ultraviolet light-emitting diodes based on Al-rich AlGaN semiconductors operating in the 210 nm-280 nm have drawn significant interest for many critical applications, including water purification, disinfection of air and surface as preventive measures of SARS COV-2, sterilization, etc. However, for the above-mentioned applications, the current technology still relies on toxic and inefficient mercury-based UV lamps. Driven by the immense need for an efficient, mercury-free, compact alternative technology, future water purification and disinfection technologies require the development of high-efficiency UV-C light-emitting diodes. To date, the external quantum efficiency (EQE) in AlGaN quantum well (QW) UV-LED heterostructures has been severely limited due to several factors including large densities of defects/dislocations, extremely low light extraction efficiency (LEE) of dominant transverse magnetic (TM) light, absorptive p -GaN contact, and total internal reflection (TIR). © 2022 IEEE.
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Preoperative events can be predicted using deep learning-based forecasting techniques. It can help to improve future decision-making. Deep learning has traditionally been used to identify and evaluate adverse risks in a variety of major applications. Numerous prediction approaches are commonly applied to deal with forecasting challenges. The number of infected people, as well as the mortality rate of COVID-19, is increasing every day. Many countries, including India, Brazil, and the United States, were severely affected;however, since the very first case was identified, the transmission rate has decreased dramatically after a set time period. Bangladesh, on the other hand, was unable to keep the rate of infection low. In this situation, several methods have been developed to forecast the number of affected, time to recover, and the number of deaths. This research illustrates the ability of DL models to forecast the number of affected and dead people as a result of COVID-19, which is now regarded as a possible threat to humanity. As part of this study, we developed an LSTM based method to predict the next 100 days of death and newly identified COVID-19 cases in Bangladesh. To do this experiment we collect data on death and newly detected COVID-19 cases through Bangladesh’s national COVID-19 help desk website. After collecting data we processed it to make a dataset for training our LSTM model. After completing the training, we predict our model with the test dataset. The result of our model is very robust on the basis of the training and testing dataset. Finally, we forecast the subsequent 100 days of deaths and newly infected COVID-19 cases in Bangladesh. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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In this study, the Traditional Convolution Neural Network (TCNN) and state-of-the-art approaches were applied to the datasets of Chest X-ray and CT scan imaging modalities and trained them concurrently. The TCNN's performance for detecting COVID-19 infected tissues was determined through a comparison examination using state-of-the-art approaches. The accuracy of the models has been improved by lowering the model's losses and overfitting. Finally, the training data size has been enhanced utilizing various picture augmentation methods such as flip-up-down, flip-down-left-right, and so on. VGG19 and InceptionV3 were tested in this work, and accuracy scores of 97 percent (X-ray images) and 96 percent (CT-scan images) were obtained. The model's loss functions, Precision, Recall, and F1-Score, were extracted and interpreted in the study. We examined the researchers' modified DL models and discovered that they were 65 percent accurate on X-ray data and 62 percent accurate on CT scan images. Experiments have demonstrated that when the number of sample images rises, the VGG19 and InceptionV3 perform well. © 2022 IEEE.
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The recent outbreak of the COVID-19 pandemic forced education institutes to shift to an internet-based online delivery mode. This unique situation accelerates a long-standing issue of digital inequality among the students in education and warrants a concentrated study to investigate students' readiness for learning in online environment. This study developed an instrument to meticulously measure the students' readiness for online learning in a pandemic situation. The proposed model consists of (a) motivation, (b) self-efficacy, and (c) situational factors. The proposed model was validated with the engineering students (for pilot study N = 68 and main study N = 988) from several universities in Bangladesh. To validate the underlying relationships between the latent constructs, an exploratory factor analysis (EFA) was performed followed by structural equation modelling (SEM) for the construct validity of the measurement model and to assess the model fit. The findings showed that besides motivation and self-efficacy, the situational factors describing the contextual dynamics emerging from the COVID-19 significantly influenced the student's online readiness. We argue that digital inequality is an important factor influencing student readiness for online learning.
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The world has been in the grips of the Coronavirus Disease-19 (COVID-19) pandemic for almost two years since December 2019. Since then the virus has infected over a hundred and fifty million and has resulted in over three million deaths. However, fatality rates have been observed to be drastically different in different countries. One reason could be the emergence of variants with differing virulence. Other factors such as demographic, health parameters, nutrition levels, and health care quality and access as well as environmental factors may contribute to the difference in fatality rates. To investigate the level of contributions of these different factors on mortality rates, we proposed a regression model using deep neural network to analyze health, nutrition, demographic, and environmental parameters during the COVID-19 lockdown period. We have used this model as it can address multivariate prediction problems with higher accuracy. The model has proved very useful in making associations and predictions with low Mean Absolute Error (MAE). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Stress has become one of the mental health adversaries of the COVID-19 pandemic. Several stressors like fear of infection, lockdown, and social distancing are commonly accountable for the stress. The existing stress prediction systems are less compatible to handle diversly changing stressors during COVID-19. The traditional approaches often use incomplete features from limited sources (e.g., only wearable sensor or user device) and static prediction techniques. The Edge Artificial Intelligence (Edge AI) employs machine learning to make data from these sources usable for decision making. Therefore, In this study, we propose a Digital Twin of Mental Stress (DTMS) model that employs IoT-based multimodal sensing and machine learning for mental stress prediction. We obtained 98% accuracy for four widely used Machine Learning(ML) algorithms Naïve Bayes(NB), Random Forest(RF), Multilayer Perceptron(MLP), and Decision Tree (DT). The optimal Digital Twin Features (DTF) could reduce the classification time. © 2021 IEEE.
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Epidemic outbreaks are collective effects of ongoing globalization, urbanisation, population mobility, climate change, demographic change and evolution of newer strains of infectious agents that result in high morbidity, mortality and huge financial loss, such as COVID-19. Thus, the early prediction of the emergence of a disease can play a pivotal role to prevent a disease to become epidemic. The Edge AI based solution has been proposed for healthcare prediction using machine learning (ML). In this paper, our focus is to propose ML based advanced model for public healthcare to reduce and control epidemic outbreaks. Collective knowledge from interconnected disciplines, shared data repository, and diverse roles have been embedded into the proposed framework. An evaluation based on actual COVID-19 related data demonstrates that ML can be used for COVID risk prediction for public health data as well as to take preventive steps to combat epidemics in early-stage. © 2021 IEEE.
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Purpose: This study aims to investigate the impact of firms' information system management capabilities on competitive performance for achieving sustainable development goals (SDGs). It also examines the moderating effects of multi-sensory stimuli capability on firms' competitive performance. Design/methodology/approach: Drawing upon the resource base and dynamic capability view as the overarching theoretical framework, this research conducted an empirical study among manufacturing and services enterprise employees. This study applied multiple cross-sectional surveys for data collection. A total of 241 usable data were obtained and explained through structural equation modelling (SEM). Findings: The statistical results explore that variables under their respective direct relationship are positively and significantly influence. Interestingly, firms information system management capability has a relatively large magnitude of positive and direct effects on the competitive performance of firms' that complement on achieving firms SDGs and coping with the COVID-19 pandemic. In addition, the multisensory stimulus capability of service firms positively moderates (amplifies) the relationship between marketing information system management capability and competitive performance. Practical implications: The proposed research model provides insights into the utilisation of firms information system management capability to achieve competitive performance in their relevant industry. In addition, it deepens the understanding of the contingency effect of using multisensory stimulus capability of firms on competitive performance. Originality/value: To the best of the authors' knowledge, drawing on the resource-based theory and dynamic capability theory, this study is the first to assess and examine the influence of firms information system management capability on the competitive performance of firms by considering the moderating variables (i.e. multisensory stimulus capability) in context to COVID-19 pandemic by considering the scope of SDGs. © 2021, Emerald Publishing Limited.
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A novel coronavirus, namely SARS-CoV-2, has emerged rapidly and overspread worldwide, causing a pandemic disease, COVID-19. Until now, no pharmaceutical interventions specific to the COVID-19 infection has been proven effective. In these circumstances, non-pharmaceutical interventions, for example, banning local and international flights, national lockdowns of cities, social distancing, self-isolation, home-quarantine, the closure of schools and universities, closure of government and private offices, banning of mass gatherings would play a vital role in minimizing the basic reproduction number (R0) in expected level. Many Asia Pacific countries, Bangladesh, China, India, Iran, Nepal, New Zealand, Pakistan, and Vietnam, adopt, practice, and implement those non-pharmaceutical interventions and have success stories. Thereby, non-pharmaceutical interventions can contain the virus's spreading, which further reduces long waiting for the healthcare system's hospitalization and burden.
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In the 21st century, any pandemic, especially, SARS-CoV-2 is a global burden due to high incidence, mortality, and mutation rate. Although several techniques have already been identified to control the pandemic or treat patients and causes of adverse impact on mental health, relatively only, fewer researchers have little concern about finding effective mitigation strategies to improve mental health. Therefore, this study aimed to find some common and unique approaches to manage mental health during a pandemic. Some strategies for the better management of mental health induced by SARS-CoV-2 infections are required for all classes of peoples. Early management is vital, and those must be associated with frontline workers and people staying at home, particularly in isolation centers and already identified as active cases. Experts have pointed out the need to pay specific attention to proper daily life. To manage abnormal mental conditions, such as anxiety, mood, personality, and psychotic disorder during the pandemic;social media, meditation, and psychological motivation with adequate diet, exercise, and sleep have significant roles in regulating some biological mechanism, incredibly immune, hormonal, and neural process. Management of mental health is mandatory for all at the time of the SARS-CoV-2 pandemic. We can consider all of the strategies mentioned above to treat mental health during and after the COVID-19 pandemic condition. © 2021,Bangladesh Society for Microbiology, Immunology and Advanced Biotechnology. All rights reserved.