A review of modern technologies for tackling COVID-19 pandemic

Kumar, Aishwarya; Gupta, Puneet Kumar; Srivastava, Ankita

doi:10.1016/j.dsx.2020.05.008

Cited by 322 publications

(218 citation statements)

References 19 publications

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“…Technology has proven to be a useful tool and a captive road. In fact, it has achieved a kind of victory over the disease itself and paves the way to a new approach in teaching (Kumar et al, 2020). The present study focuses on the effect of COVID-19 on the teaching process via GM.…”

Section: Discussionmentioning

confidence: 99%

Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic

Al-Maroof

Salloum

Hassanien

et al. 2020

Interactive Learning Environments

199

128

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This study seeks to explore the effect of fear emotion on students' and teachers' technology adoption during COVID-19 pandemic. The study has made use of Google Meet© as an educational social platform in private higher education institutes. The data obtained from the study were analyzed by using the partial least squares structural equation modeling (PLS-SEM) and machine learning algorithms. The main hypotheses of this study are related to the effect of COVID-19 on the adoption of Google Meet as COVID-19 rise s various types of fear. During the Coronavirus pandemic, fear due to family lockdown situation, fear of education failure and fear of losing social relationships are the most common types of threat that may face students and teachers/educators. These types of fears are connected with two important factors within TAM theory, which are: perceived ease of use (PEOU) and perceived usefulness (PU), and with another external factor of TAM, which is subjective norm (SN). The results revealed that both data analysis techniques have successfully provided support to all the hypothesized relationships of the research model. More interesting, the J48 classifier has performed better than the other classifiers in predicting the dependent variable in most cases.

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Section: Discussionmentioning

confidence: 99%

Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic

Al-Maroof

Salloum

Hassanien

et al. 2020

Interactive Learning Environments

199

128

View full text Add to dashboard Cite

show abstract

“…In Hanumanthu [69], deep learning approaches that were used in different problem domains showed that the accuracy ranging from a minimum of 85% and 98.3%. Similarly, Kumar, Gupta [62] identified the accuracy of AI-based technologies as ranging from 79.3% to 95%. The reason for variation in accuracy is due to data availability on factors which are not limited to age and chronic disease, and different models' parameters.…”

Section: Contact Tracing With Artificial Intelligence Modelsmentioning

confidence: 99%

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

Agbehadji

Awuzie

Ngowi

et al. 2020

IJERPH

199

112

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The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19’s cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.

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“…Some of the leading hospitals across the world are utilizing AI/ML algorithms to diagnose COVID-19 cases from CT scans/X-ray images after preliminary trails of the technology. 1 Efforts have been made on surveying the role of ICT in combating COVID-19 pandemic [26,27]. Specifically, the role of AI, data science, and big data in the management of COVID-19 has been surveyed.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 open source data sets: a comprehensive survey

et al. 2020

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In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligencebased analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.

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A review of modern technologies for tackling COVID-19 pandemic

Cited by 322 publications

References 19 publications

Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic

Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

COVID-19 open source data sets: a comprehensive survey

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