A Systematic Review of the Digital Interventions for Fighting COVID-19: The Bangladesh Perspective

BT-CNN: a balanced binary tree architecture for classification of brain tumour using MRI imaging

Deep learning is a very important technique in clinical diagnosis and therapy in the present world. Convolutional Neural Network (CNN) is a recent development in deep learning that is used in computer vision. Our medical investigation focuses on the identification of brain tumour. To improve the brain tumour classification performance a Balanced binary Tree CNN (BT-CNN) which is framed in a binary tree-like structure is proposed. It has a two distinct modules-the convolution and the depthwise separable convolution group. The usage of convolution group achieves lower time and higher memory, while the opposite is true for the depthwise separable convolution group. This balanced binarty tree inspired CNN balances both the groups to achieve maximum performance in terms of time and space. The proposed model along with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. are experimented on public datasets. Before we feed the data into model the images are pre-processed using CLAHE, denoising, cropping, and scaling. The pre-processed dataset is partitioned into training and testing datasets as per 5 fold cross validation. The proposed model is trained and compared its perforarmance with state-of-the-art models like CNN-KNN and models proposed by Musallam et al., Saikat et al., and Amin et al. The proposed model reported average training accuracy of 99.61% compared to other models. The proposed model achieved 96.06% test accuracy where as other models achieved 68.86%, 85.8%, 86.88%, and 90.41% respectively. Further, the proposed model obtained lowest standard deviation on training and test accuracies across all folds, making it invariable to dataset.

Postacute Sequelae of COVID-19 and Adverse Psychiatric Outcomes: Protocol for an Etiology and Risk Systematic Review

BACKGROUND

The postacute sequelae of COVID-19 (PASC) is a syndrome characterized by persistent COVID-19 symptoms or the onset of new symptoms following recovery from the initial or acute phase of the illness. Such symptoms often occur 4 or more weeks after being diagnosed with COVID-19. Although a lot of work has gone into understanding the long-term mental health effects of PASC, many questions related to the etiology and risk of this condition remain.

OBJECTIVE

This protocol is for a systematic review assessing the association between PASC and adverse psychiatric outcomes and whether people with PASC are at greater risk of developing an adverse psychiatric outcome than those without PASC.

METHODS

Various medical literature databases (eg, PubMed and EMBASE) will be searched for eligible articles, using predefined search criteria. Gray literature will also be explored. Epidemiological observational studies and secondary analyses of randomized controlled trials that report a quantitative relationship between PASC and at least one adverse psychiatric outcome will be included. The Population, Exposure of interest, Comparator, and Outcome framework will be used as a standardized framework for the inclusion criteria. The Joanna Briggs Institute critical appraisal tools will be used to assess methodological quality and critically appraise the risk of bias in included studies. A random-effects meta-analysis will be conducted if possible. A formal narrative synthesis will be performed if a meta-analysis is impossible due to substantial heterogeneity across studies. The Grading of Recommendations Assessment, Development and Evaluation approach will be used to rate the cumulative certainty of the evidence for all outcomes. Ethical approval is not required. The study results will be published in a peer-reviewed journal.

RESULTS

This study documents and addresses etiology, risk factors, and long-term symptoms of COVID-19 among people with PASC. It focuses on a key priority area for new evidence syntheses on the clinical management of COVID-19 and pandemic-related conditions. It will include evidence on nonhospitalized and hospitalized patients with a history of PASC.

CONCLUSIONS

Substantial heterogeneity across studies may limit the ability to perform a meta-analysis. Findings will inform disease prevention, decision-making, health care policy, and clinical research (Reviewed by the Plan P #PeerRef Community).

TRIAL REGISTRATION

PROSPERO CRD42022308737; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308737.

Predictis: an IoT and machine learning-based system to predict risk level of cardio-vascular diseases

BACKGROUND

Despite technological advancement in the field of healthcare, the worldwide burden of illness caused by cardio-vascular diseases (CVDs) is rising, owing mostly to a sharp increase in developing nations that are undergoing fast health transitions. People have been experimenting with techniques to extend their lives since ancient times. Despite this, technology is still a long way from attaining the aim of lowering mortality rates.

METHODS

From methodological perspective, a design Science Research (DSR) approach is adopted in this research. As such, to investigate the current healthcare and interaction systems created for predicting cardiac disease for patients, we first analyzed the body of existing literature. After that, a conceptual framework of the system was designed using the gathered requirements. Based on the conceptual framework, the development of different components of the system was completed. Finally, the evaluation study procedure was developed taking into account the effectiveness, usability and efficiency of the developed system.

RESULTS

To attain the objectives, we proposed a system consisting of a wearable device and mobile application, which allows the users to know their risk levels of having CVDs in the future. The Internet of Things (IoT) and Machine Learning (ML) techniques were adopted to develop the system that can classify its users into three risk levels (high, moderate and low risk of having CVD) with an F1 score of 80.4% and two risk levels (high and low risk of having CVD) with an F1 score of 91%. The stacking classifier incorporating best-performing ML algorithms was used for predicting the risk levels of the end-users utilizing the UCI Repository dataset.

CONCLUSION

The resultant system allows the users to check and monitor their possibility of having CVD in near future using real-time data. Also, the system was evaluated from the Human-Computer Interaction (HCI) point of view. Thus, the created system offers a promising resolution to the current biomedical sector.

TRIAL REGISTRATION

Not Applicable.

Digitalization impacts the COVID-19 pandemic and the stringency of government measures

COVID-19 poses a significant burden to populations worldwide. Although the pandemic has accelerated digital transformation, little is known about the influence of digitalization on pandemic developments. Therefore, this country-level study aims to explore the impact of pre-pandemic digital adoption on COVID-19 outcomes and government measures. Using the Digital Adoption Index (DAI), we examined the association between countries' digital preparedness levels and COVID-19 cases, deaths, and stringency indices (SI) of government measures until March 2021. Gradient Tree Boosting based algorithm pinpointed essential features related to COVID-19 trends, such as digital adoption, populations' smoker fraction, age, and poverty. Subsequently, regression analyses indicated that higher DAI was associated with significant declines in new cases (β = - 362.25/pm; p < 0.001) and attributed deaths (β = - 5.53/pm; p < 0.001) months after the peak. When plotting DAI against the SI normalized for the starting day, countries with higher DAI adopted slightly more stringent government measures (β = 4.86; p < 0.01). Finally, a scoping review identified 70 publications providing valuable arguments for our findings. Countries with higher DAI before the pandemic show a positive trend in handling the pandemic and facilitate the implementation of more decisive governmental measures. Further distribution of digital adoption may have the potential to attenuate the impact of COVID-19 cases and deaths.

Leveraging machine learning to analyze sentiment from COVID-19 tweets: A global perspective

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID-19 pandemic from a cross-country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8%) F1-score, followed by gradient boost (84.3%), AdaBoost (78.9%), and XGBoost (83.1%). Second, it was revealed that during the time of the COVID-19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like.

Coping with pandemics using social network sites: A psychological detachment perspective to COVID-19 stressors

Prior research has often portrayed information technology (IT) as a stressor. In this paper, we propose and demonstrate that IT can also be an effective means of coping with life stressors, including those induced by pandemics such as COVID-19. We thus deviate from the common IT-as-a-stressor perspective and adopt an IT-as-a-coping-mechanism viewpoint. To this end, we apply the stressor-detachment model from organisational psychology to the use of social network sites (SNSs) in coping with stressors wrought by the COVID-19 pandemic. We examine psychological well-being as our dependant variable and introduce psychological detachment through SNS use as a mediator and moderator of the associations between psychological well-being and two COVID-19 stressors: work-family conflict and perceived isolation. We used structural equation modelling and tested this model with survey data collected from 398 professionals who were in lockdown and working from home during the pandemic. The results indicated that psychological detachment through SNS uses increased psychological well-being and that heightened work-family conflict motivated this detachment strategy. In contrast, consistent with helplessness and motivation-opportunity theories, perceived isolation as a stressor did not influence psychological detachment through SNS use. While perceived isolation directly reduced individual well-being, the effect of work-family conflict on well-being was contingent upon users' levels of psychological detachment through SNS use. These findings suggest that while psychological detachment through SNS use is an effective means of improving one's well-being, it can be positively or negatively affected by stressors. Our study contributes to research on technology-mediated strategies for coping with stress and the psychosocial implications of global pandemics.

A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic

The unprecedented outbreak of the Corona Virus Disease 2019 (COVID-19) pandemic has seriously affected numerous countries in the world from various aspects such as education, economy, social security, public health, etc. Most governments have made great efforts to control the spread of COVID-19, e.g., locking down hard-hit cities and advocating masks for the population. However, some countries and regions have relatively poor medical conditions in terms of insufficient medical equipment, hospital capacity overload, personnel shortage, and other problems, resulting in the large-scale spread of the epidemic. With the unique advantages of Artificial Intelligence (AI), it plays an extremely important role in medical imaging, clinical data, drug development, epidemic prediction, and telemedicine. Therefore, AI is a powerful tool that can help humans solve complex problems, especially in the fight against COVID-19. This study aims to analyze past research results and interpret the role of Artificial Intelligence in the prevention and treatment of COVID-19 from five aspects. In this paper, we also discuss the future development directions in different fields and prove the validity of the models through experiments, which will help researchers develop more efficient models to control the spread of COVID-19.

Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda

Machine Learning (ML) has been widely used in predicting the mode of childbirth and assessing the potential maternal risks during pregnancy. The primary aim of this review study is to explore current research and development perspectives that utilizes the ML techniques to predict the optimal mode of childbirth and to detect various complications during childbirth. A total of 26 articles (published between 2000 and 2020) from an initial set of 241 articles were selected and reviewed following a Systematic Literature Review (SLR) approach. As outcomes, this review study highlighted the objectives or focuses of the recent studies conducted on pregnancy outcomes using ML; explored the adopted ML algorithms along with their performances; and provided a synthesized view of features used, types of features, data sources and its characteristics. Besides, the review investigated and depicted how the objectives of the prior studies have changed with time being; and the association among the objectives of the studies, uses of algorithms, and the features. The study also delineated future research opportunities to facilitate the existing initiatives for reducing maternal complacent and mortality rates, such as: utilizing unsupervised and deep learning algorithms for prediction, revealing the unknown reasons of maternal complications, developing usable and useful ML-based clinical decision support systems to be used by the expecting mothers and health professionals, enhancing dataset and its accessibility, and exploring the potentiality of surgical robotic tools. Finally, the findings of this review study contributed to the development of a conceptual framework for advancing the ML-based maternal healthcare system. All together, this review will provide a state-of-the-art paradigm of ML-based maternal healthcare that will aid in clinical decision-making, anticipating pregnancy problems and delivery mode, and medical diagnosis and treatment.

COVID-19 and black fungus: Analysis of the public perceptions through machine learning

While COVID-19 is ravaging the lives of millions of people across the globe, a second pandemic "black fungus" has surfaced robbing people of their lives especially people who are recovering from coronavirus. Thus, the objective of this article is to analyze public perceptions through sentiment analysis regarding black fungus during the COVID-19 pandemic. To attain the objective, first, a support vector machine (SVM) model, with an average AUC of 82.75%, was developed to classify user sentiments in terms of anger, fear, joy, and sad. Next, this SVM model was used to predict the class labels of the public tweets (n = 6477) related to COVID-19 and black fungus. As outcome, this article found public perceptions towards black fungus during COVID-19 pandemic belong mostly to sad (n= 2370, 36.59%), followed by joy (n = 2095, 32.34%), fear (n = 1914, 29.55%) and anger (n = 98, 1.51%). This article also found that public perceptions are varied to some critical concerns like education, lockdown, hospital, oxygen, quarantine, and vaccine. For example, people mostly exhibited fear in social media about education, hospital, vaccine while some people expressed joy about education, hospital, vaccine, and oxygen. Again, it was found that mass people have an ignorance tendency to lockdown, COVID-19 restrictions, and prescribed hygiene rules although the coronavirus and black fungus infection rates broke the previous infection records.

A framework for information technology-based management against COVID-19 in Iran

Background

The COVID-19 pandemic has become a global concern. Iran is one of the countries affected most by the SARS-CoV-2 outbreak. As a result, the use of information technology (IT) has a variety of applications for pandemic management. The purpose of this study was to develop a conceptual framework for responding to the COVID-19 pandemic via IT management, based on extensive literature review and expert knowledge.

Methods

The conceptual framework is developed in three stages: (1) a literature review to gather practical experience with IT applications for managing the COVID-19 pandemic, (2) a study of Iranian documents and papers that present Iran’s practical experience with COVID-19, and (3) developing a conceptual framework based on the previous steps and validating it through a Delphi approach in two rounds, and by 13 experts.

Results

The proposed conceptual framework demonstrates that during pandemics, 22 different types of technologies were used for various purposes, including virtual education, early warning, rapid screening and diagnosis of infected individuals, and data management. These objectives were classified into six categories, with the following applications highlighted: (1) Prevention (M-health, Internet search queries, telehealth, robotics, Internet of things (IoT), Artificial Intelligence (AI), big data, Virtual Reality (VR), social media); (2) Diagnosis (M-health, drones, telehealth, IoT, Robotics, AI, Decision Support System (DSS), Electronic Health Record (EHR)); (3) Treatment (Telehealth, M-health, AI, Robotic, VR, IoT); (4) Follow-up (Telehealth, M-health, VR), (5) Management & planning (Geographic information system, M-health, IoT, blockchain), and (6) Protection (IoT, AI, Robotic and automatic vehicles, Augmented Reality (AR)). In Iran, the use of IT for prevention has been emphasized through M-health, internet search queries, social media, video conferencing, management and planning objectives using databases, health information systems, dashboards, surveillance systems, and vaccine coverage.

Conclusions

IT capabilities were critical during the COVID-19 outbreak. Practical experience demonstrates that various aspects of information technologies were overlooked. To combat this pandemic, the government and decision-makers of this country should consider strategic planning that incorporates successful experiences against COVID-19 and the most advanced IT capabilities.

Ranking the health precautions for the 'new normal' after the COVID-19 outbreak in production environments

Objectives. With the outbreak of coronavirus (COVID-19) about a year ago and its quick spread all around the world, some serious decisions had to be made like halting production temporarily. The world now tries to take back its normal pace thanks to some medical improvements. However, the 'new normal' is unlikely to follow the old habits in which COVID-19 never appeared. In production environments, a number of new precautions should be defined to prevent a spread of COVID-19 disease among employees in the new normal period. The aim of this study is to propose an analytical approach to define these new precautions and prioritize them. Methods. To determine the precautions, open archive publications of the Turkish Health Ministry and the World Health Organization, and the opinions of occupational physicians and academicians were considered. Twenty-five precautions were specified under three main headings. The Pythagorean fuzzy analytical hierarchy process was employed to gain the rank of precautions. Results. The most critical precautions and sub-precautions were determined as organizational precautions and developing an appropriate working model to ensure social distance. Conclusion. Using the determined order of measures, the managers are able to apply them, starting from the most effective ones.

Addressing economic and health challenges of COVID-19 in Bangladesh: Preparation and response

This article mainly explores the economic and health challenges faced by Bangladesh amid COVID-19 and the policies taken by the government of Bangladesh to tackle the economic and health issues. Bangladesh is ranked as one of the worst-hit countries in terms of total corona infections. Affecting the social, economic, and health sectors of the country, COVID-19 pandemic has dampened the overall economic well-being and thus GDP growth along with skyrocketing poverty, inequality, and unemployment nationwide. To tackle these crises, the government has initiated effective policy measures which, in turn, enhanced the recovery rate of COVID-19 positive patients and strengthened the recovery of economic indicators. Therefore, this article suggests other hard-hit COVID-19 affected countries following the recovery model of Bangladesh to encounter the economic and health challenges due to the coronavirus pandemic.

Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases-A Systematic Literature Review

With the growing concern about the spread of new respiratory infectious diseases, several studies involving the application of technology in the prevention of these diseases have been carried out. Among these studies, it is worth highlighting the importance of those focused on the primary forms of prevention, such as social distancing, mask usage, quarantine, among others. This importance arises because, from the emergence of a new disease to the production of immunizers, preventive actions must be taken to reduce contamination and fatalities rates. Despite the considerable number of studies, no records of works aimed at the identification, registration, selection, and rigorous analysis and synthesis of the literature were found. For this purpose, this paper presents a systematic review of the literature on the application of technological solutions in the primary ways of respiratory infectious diseases transmission prevention. From the 1139 initially retrieved, 219 papers were selected for data extraction, analysis, and synthesis according to predefined inclusion and exclusion criteria. Results enabled the identification of a general categorization of application domains, as well as mapping of the adopted support mechanisms. Findings showed a greater trend in studies related to pandemic planning and, among the support mechanisms adopted, data and mathematical application-related solutions received greater attention. Topics for further research and improvement were also identified such as the need for a better description of data analysis and evidence.

Understanding the impact of information sources on COVID-19 related preventive measures in Finland

The COVID-19 pandemic amplified the influence of information reporting on human behavior, as people were forced to quickly adapt to a new health threatening situation by relying on new information. Drawing from protection-motivation and cognitive load theories, we formulated a structural model eliciting the impact of the three online information sources: (1) social media, (2) official websites, and (3) other online news sources; on motivation to adopt recommended COVID-19 preventive measures. The model was tested with the data collected from university employees and students (n = 225) in March 2020 through an online survey and analyzed using partial least square structural equation modeling (PLS-SEM). We observed that social media and other online news sources increased information overload amongst the online information sources. This, in turn, negatively affected individuals' self-isolation intention by increasing perceived response costs and decreasing response efficacy. The study highlights the role of online information sources on preventive behaviors during pandemics.

COVID-19-related online misinformation in Bangladesh

Purpose

This paper aims to understand the popular themes of coronavirus disease 2019 (COVID-19)-related online misinformation in Bangladesh and to provide some suggestions to abate the problem.

Design/methodology/approach

This paper discusses online COVID-19-related misinformation in Bangladesh. Following thematic analyses, the paper discusses some dominant misinformation themes based on the data collected from three fact-checking websites of Bangladesh run by media professionals and scholars.

Findings

COVID-19-related online misinformation in Bangladesh has six popular themes: health, political, religious, crime, entertainment and miscellaneous. To curb misinformation, many initiatives have been taken so far that have produced little success. This paper briefly proposes the implementation of an experimental two-way misinformation prevention technique for a better result.

Originality/value

Acknowledging previous initiatives, this paper discusses the major themes and offers additional solutions to reduce online misinformation which would benefit academics as well as policymakers.

Consumer side economic perception of telemedicine during COVID-19 era: A survey on Bangladesh's perspective

In Bangladesh, the telemedicine industry is one of the few industries able to flourish in the contemporary era of COVID-19. But to thrive, the industry must know the viewpoints of both consumers (those who are interested in availing the services of the industry) and non-consumers to overcome deficits. This should be done to maximize profits and give optimal utility to users so that the industry can be made sustainable in the long run. The main aim of this paper is to analyze the economic perception of both the telemedicine consumers and non-consumers of Bangladesh and the actions required to be taken to optimize them. A survey was developed with 18 questions divided into several parts relating to the health identity of the respondent, the respondents' use of telemedicine, the analysis of the economic behaviors of the respondents with regards to telemedicine, and the consumer perception of the merits and demerits of telemedicine. The survey results show that about one-third has used some form of telemedicine during the COVID-19 pandemic. Among the telemedicine users, 48% used hospital-mandated telemedicine services whereas 41% used mobile telemedicine applications. The survey states that 75% were satisfied with the service they received. The average payment made by the respondent population was 532 Taka, and 62% of them thought that the amount they paid was justified. In conclusion, the results of this survey can be utilized in making economically viable telemedicine models that will give optimal utility to its consumers and help forecast the next stage of the industry for betterment in the health sector.

Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective

Social media plays a significant role during pandemics such as COVID-19, as it enables people to share news as well as personal experiences and viewpoints with one another in real-time, globally. Building off the affordance lens and cognitive load theory, we investigate how motivational factors and personal attributes influence social media fatigue and the sharing of unverified information during the COVID-19 pandemic. Accordingly, we develop a model which we analyse using the structural equation modelling and neural network techniques with data collected from young adults in Bangladesh (N = 433). The results show that people, who are driven by self-promotion and entertainment, and those suffering from deficient self-regulation, are more likely to share unverified information. Exploration and religiosity correlated negatively with the sharing of unverified information. However, exploration also increased social media fatigue. Our findings indicate that the different use purposes of social media introduce problematic consequences, in particular, increased misinformation sharing.

The COVID-19 pandemic and the public health challenges in Bangladesh: a commentary

Purpose

The purpose of this paper is to highlight the current pandemic situation of coronavirus disease 2019 (COVID-19) in Bangladesh, how the Government is managing this unprecedented condition and encountering these increasing public health challenges.

Design/methodology/approach

This paper is a viewpoint of the current COVID-19 pandemic situation in Bangladesh.

Findings

As one of the most densely populated countries, Bangladesh is vulnerable to COVID-19. Currently, the infection of COVID-19 is spreading fast and started to capture all the parts of Bangladesh. The Government of Bangladesh has already taken several preventive measures to overcome the pandemic such as declaring hotspots of COVID-19 and setting lockdowns, increasing mass awareness through social media and satellite TV channels. They are also encouraging private and community healthcare initiatives to increase hospital beds and COVID-19 treatment facilities. Besides, the Government has deployed defence force and additional health workers and increased public holidays to reduce the number of coronavirus infections. However, both the number of infected people and the death toll is rising, and there are growing challenges that the Government and public healthcare professions need to overcome.

Originality/value

This paper delivers information about the present developing situation of COVID-19 in Bangladesh, how the Government is handling and public health challenges that have raised. This paper can be helpful for the policymakers and Government officials for effective public health interventions.

A Review on the Mobile Applications Developed for COVID-19: An Exploratory Analysis

The objective of this research is to explore the existing mobile applications developed for the COVID-19 pandemic. To obtain this research objective, firstly the related applications were selected through the systematic search technique in the popular application stores. Secondly, data related to the app objectives, functionalities provided by the app, user ratings, and user reviews were extracted. Thirdly, the extracted data were analyzed through the affinity diagram, noticing-collecting-thinking, and descriptive analysis. As outcomes, the review provides a state-of-the-art view of mobile apps developed for COVID-19 by revealing nine functionalities or features. It revealed ten factors related to information systems design characteristics that can guide future app design. The review outcome highlights the need for new development and further refinement of the existing applications considering not only the revealed objectives and their associated functionalities, but also revealed design characteristics such as reliability, performance, usefulness, supportive, security, privacy, flexibility, responsiveness, ease of use, and cultural sensitivity.