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1.
Malaysian Journal of Medical Sciences ; 29(6):15-33, 2022.
Article in English | EMBASE | ID: covidwho-2204905

ABSTRACT

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. Copyright © 2022, Penerbit Universiti Sains Malaysia. All rights reserved.

2.
PLoS ONE [Electronic Resource] ; 18(1):e0278134, 2023.
Article in English | MEDLINE | ID: covidwho-2197037

ABSTRACT

We previously reported that SARS-CoV-2 infection reduces human nasopharyngeal commensal microbiomes (bacteria, archaea and commensal respiratory viruses) with inclusion of pathobionts. This study aimed to assess the possible changes in the abundance and diversity of resident mycobiome in the nasopharyngeal tract (NT) of humans due to SARS-CoV-2 infections. Twenty-two (n = 22) nasopharyngeal swab samples (including COVID-19 = 8, Recovered = 7, and Healthy = 7) were collected for RNA-sequencing followed by taxonomic profiling of mycobiome. Our analyses indicate that SARS-CoV-2 infection significantly increased (p < 0.05, Wilcoxon test) the population and diversity of fungi in the NT with inclusion of a high proportion of opportunistic pathogens. We detected 863 fungal species including 533, 445, and 188 species in COVID-19, Recovered, and Healthy individuals, respectively that indicate a distinct mycobiome dysbiosis due to the SARS-CoV-2 infection. Remarkably, 37% of the fungal species were exclusively associated with SARS-CoV-2 infection, where S. cerevisiae (88.62%) and Phaffia rhodozyma (10.30%) were two top abundant species. Likewise, Recovered humans NT samples were predominated by Aspergillus penicillioides (36.64%), A. keveii (23.36%), A. oryzae (10.05%) and A. pseudoglaucus (4.42%). Conversely, Nannochloropsis oceanica (47.93%), Saccharomyces pastorianus (34.42%), and S. cerevisiae (2.80%) were the top abundant fungal species in Healthy controls nasal swabs. Importantly, 16% commensal fungal species found in the Healthy controls were not detected in either COVID-19 patients or when they were cured from COVID-19 (Recovered). We also detected several altered metabolic pathways correlated with the dysbiosis of fungal mycobiota in COVID-19 patients. Our results suggest that SARS-CoV-2 infection causes significant dysbiosis of mycobiome and related metabolic functions possibly play a determining role in the progression of SARS-CoV-2 pathogenesis. These findings might be helpful for developing mycobiome-based diagnostics, and also devising appropriate therapeutic regimens including antifungal drugs for prevention and control of concurrent fungal coinfections in COVID-19 patients.

3.
International Journal of Physical Distribution & Logistics Management ; 2022.
Article in English | Web of Science | ID: covidwho-2191441

ABSTRACT

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.

4.
Annals of Operations Research ; : 1-29, 2022.
Article in English | MEDLINE | ID: covidwho-2174471

ABSTRACT

Social media (SM) fake news has become a serious concern especially during COVID-19. In this study, we develop a research model to investigate to what extent SM fake news contributes to supply chain disruption (SCD), and what are the different SM affordances that contribute to SM fake news. To test the derived hypotheses with survey data, we have applied partial least square based structural equation modelling (PLS-SEM) technique. Further, to identify how different configurations of SC resilience (SCR) capabilities reduce SCD, we have used fuzzy set qualitative comparative analysis (fsQCA). The results show that SM affordances lead to fake news, which increases consumer panic buying (CPB);CPB in turn increases SCD. In addition, SM fake news directly increases SCD. The moderation test suggests that, SCR capability, as a higher-order construct, decreases the effect of CPB on SCD;however, neither of the capabilities individually moderates. Complimentarily, the fsQCA results suggest that no single capability but their three specific configurations reduce SCD. This work offers a new theoretical perspective to study SCD through SM fake news. Our research advances the knowledge of SCR from a configurational lens by adopting an equifinal means towards mitigating disruption. This research will also assist the operations and SC managers to strategize and understand which combination of resilience capabilities is the most effective in tackling disruptions during a crisis e.g., COVID-19. In addition, by identifying the relative role of different SM affordances, this study provides pragmatic insights into SM affordance measures that combat fake news on SM.

5.
New Gener Comput ; : 1-20, 2023.
Article in English | Web of Science | ID: covidwho-2174090

ABSTRACT

Social distancing is considered as the most effective prevention techniques for combatting pandemic like Covid-19. It is observed in several places where these norms and conditions have been violated by most of the public though the same has been notified by the local government. Hence, till date, there has been no proper structure for monitoring the loyalty of the social-distancing norms by individuals. This research has proposed an optimized deep learning-based model for predicting social distancing at public places. The proposed research has implemented a customized model using detectron2 and intersection over union (IOU) on the input video objects and predicted the proper social-distancing norms continued by individuals. The extensive trials were conducted with popular state-of-the-art object detection model: regions with convolutional neural networks (RCNN) with detectron2 and fast RCNN, RCNN with TWILIO communication platform, YOLOv3 with TL, fast RCNN with YOLO v4, and fast RCNN with YOLO v2. Among all, the proposed (RCNN with detectron2 and fast RCNN) delivers the efficient performance with precision, mean average precision (mAP), total loss (TL) and training time (TT). The outcomes of the proposed model focused on faster R-CNN for social-distancing norms and detectron2 for identifying the human 'person class' towards estimating and evaluating the violation-threat criteria where the threshold (i.e., 0.75) is calculated. The model attained precision at 98% approximately (97.9%) with 87% recall score where intersection over union (IOU) was at 0.5.

6.
Mathematical Biosciences and Engineering ; 20(1):1083-1105, 2023.
Article in English | Scopus | ID: covidwho-2143972

ABSTRACT

Rapid diagnosis to test diseases, such as COVID-19, is a significant issue. It is a routine virus test in a reverse transcriptase-polymerase chain reaction. However, a test like this takes longer to complete because it follows the serial testing method, and there is a high chance of a false-negative ratio (FNR). Moreover, there arises a deficiency of R.T.-PCR test kits. Therefore, alternative procedures for a quick and accurate diagnosis of patients are urgently needed to deal with these pandemics. The infrared image is self-sufficient for detecting these diseases by measuring the temperature at the initial stage. C.T. scans and other pathological tests are valuable aspects of evaluating a patient with a suspected pandemic infection. However, a patient's radiological findings may not be identified initially. Therefore, we have included an Artificial Intelligence (A.I.) algorithm-based Machine Intelligence (MI) system in this proposal to combine C.T. scan findings with all other tests, symptoms, and history to quickly diagnose a patient with a positive symptom of current and future pandemic diseases. Initially, the system will collect information by an infrared camera of the patient's facial regions to measure temperature, keep it as a record, and complete further actions. We divided the face into eight classes and twelve regions for temperature measurement. A database named patient-info-mask is maintained. While collecting sample data, we incorporate a wireless network using a cloudlets server to make processing more accessible with minimal infrastructure. The system will use deep learning approaches. We propose convolution neural networks (CNN) to cross-verify the collected data. For better results, we incorporated tenfold cross-verification into the synthesis method. As a result, our new way of estimating became more accurate and efficient. We achieved 3.29% greater accuracy by incorporating the "decision tree level synthesis method" and "ten-folded-validation method". It proves the robustness of our proposed method. © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)

7.
2022 Photonics North, PN 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2120643

ABSTRACT

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.

8.
1st International Conference on 4th Industrial Revolution and Beyond, IC4IR 2021 ; 437:551-561, 2022.
Article in English | Scopus | ID: covidwho-2094497

ABSTRACT

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.

9.
7th IEEE International conference for Convergence in Technology, I2CT 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1992601

ABSTRACT

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.

10.
Educational Technology & Society ; 25(3):30-45, 2022.
Article in English | Web of Science | ID: covidwho-1980166

ABSTRACT

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.

11.
Asian Pacific Journal of Reproduction ; 11(4):155-157, 2022.
Article in English | EMBASE | ID: covidwho-1979493
12.
36th International Conference on Advanced Information Networking and Applications, AINA 2022 ; 450 LNNS:329-338, 2022.
Article in English | Scopus | ID: covidwho-1826236

ABSTRACT

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.

13.
2021 IEEE Globecom Workshops, GC Wkshps 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1746094

ABSTRACT

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.

14.
2021 IEEE Globecom Workshops, GC Wkshps 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1746093

ABSTRACT

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.

15.
Journal of Enterprise Information Management ; 2021.
Article in English | Scopus | ID: covidwho-1470248

ABSTRACT

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.

16.
Transplant International ; 34:398-399, 2021.
Article in English | Web of Science | ID: covidwho-1396287
18.
Transplant International ; 34:351-351, 2021.
Article in English | Web of Science | ID: covidwho-1396039
19.
Asia Pacific Journal of Health Management ; 16(2):94-99, 2021.
Article in English | Web of Science | ID: covidwho-1329530

ABSTRACT

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.

20.
Journal of Advanced Biotechnology and Experimental Therapeutics ; 4(3):276-289, 2021.
Article in English | Scopus | ID: covidwho-1304825

ABSTRACT

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.

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