Data Science for COVID-19

Volume 2: Societal and Medical Perspectives

1st Edition - October 22, 2021
Editors: Utku Kose, Deepak Gupta, Victor Hugo Costa de Albuquerque, Ashish Khanna
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 7 6 9 - 9
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 7 7 0 - 5

Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techni… Read more

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Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques for the detection, mitigation, treatment and elimination of the COVID-19 virus. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques, including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes is now possible.

Cover image
Title page
Table of Contents
Copyright
Contributors
Foreword
Preface
1. Essentials of the COVID-19 coronavirus
1. Introduction
2. Materials and methods
3. Revealed essential features of COVID-19 coronavirus
Abbreviations
2. Docking study of transmembrane serine protease type 2 inhibitors for the treatment of COVID-19
1. Introduction
2. Materials and methods
3. Results
4. Discussion
5. Conclusions
3. Gut-lung cross talk in COVID-19 pathology and fatality rate
1. Introduction
2. Adult human gut microbiota
3. Respiratory tract microbiota
4. Gut-lung cross talk during viral COVID-19
5. Suggested COVID-19 intervention strategies through the use of probiotics and prebiotics
6. The role of probiotic in ventilator-associated pneumonia
4. Data sharing and privacy issues arising with COVID-19 data and applications
1. Introduction
2. The process of accelerating COVID-19 research
3. Medical data and sharing
4. COVID-19 applications and privacy
5. Discussion and suggestions for further research
5. COVID-19 outlook in the United States of America: a data-driven thematic approach
1. Introduction
2. Sociotechnical theory
3. Methodology
4. Results
5. Discussions
6. Conclusion
6. Artificial intelligence and COVID-19: fighting pandemics
1. Introduction
2. Phases for fighting pandemics
3. Present artificial intelligence efforts for fighting COVID-19
4. Ethical use of artificial intelligence while fighting COVID-19
5. Ongoing lessons from COVID-19
6. Concluding remarks
7. Data science: a survey on the statistical analysis of the latest outbreak of the 2019 pandemic novel coronavirus disease (COVID-19) using ANOVA
1. Introduction
2. Background
3. Overview of dataset
4. Statistical analysis
5. Outbreak of COVID-19, as of March 31, 2020
6. Outbreak of COVID-19, as of April 25, 2020
7. Comparison of COVID-19 in March and April
8. Conclusion
8. Application of big data in COVID-19 epidemic
1. Introduction
2. The growth of data in healthcare
3. Big data privacy and ethical challenges in COVID-19
4. Big data analytics in COVID-19 epidemic
5. Conclusion
9. Artificial intelligence-based solutions for COVID-19
1. Introduction
2. Technologic solutions to help combat the COVID-19 outbreak
3. Limitations and future scope
10. Telemedicine applications for pandemic diseases, with a focus on COVID-19
1. Introduction
2. Telemedicine applications during epidemic/pandemic
3. Telemedicine applications for COVID-19
4. Discussion
5. Conclusions and future work
11. Impact of COVID-19 and lockdown policies on farming, food security, and agribusiness in West Africa
1. Introduction
2. Methods
3. Results
4. Discussion
5. Conclusion and recommendations
12. Study and impact analysis of COVID-19 pandemic clinical data on infection spreading
1. Introduction
2. Related work
3. Clinical data analysis
4. Case study
5. Proposed model for the prediction of COVID-19
6. Conclusion and future scope
13. Toward analyzing the impact of healthcare treatments in industry 4.0 environment—a self-care case study during COVID-19 outbreak
1. Introduction
2. Case study: experimental simulation, results, and analysis
3. Conclusion
14. Big data processing and analysis on the impact of COVID-19 on public transport delay
1. Introduction
2. Data preparation
3. Methodology
4. Case study
5. Conclusion and future work
15. The role of societal research and centers in analyzing society in pandemic times
1. Introduction
2. Materials and methods
3. Findings
4. Discussion
5. Conclusions and suggestions/future work
16. Modeling and predicting the spread of COVID-19: a continental analysis
1. Introduction
2. A continental review of modeling and prediction studies
3. Discussion
4. Conclusion
17. Applications of Building Information Modeling for COVID-19 spread assessment due to the organization of building artifacts
1. Introduction
2. Summarizing the applications of Building Information Modeling for CSAOBA
3. An add-in tool for building-level communal spread due to seating arrangements within a room
4. Case study
5. Discussion
6. Conclusion
18. COVID-19 diagnosis—myths and protocols
1. Introduction
2. Diagnostic methods
3. Conclusion
19. “Quarantined within a quarantine”: COVID-19 and GIS Dynamic Scenario Modeling in Tasmania, Australia
1. Introduction
2. Managing pandemics with data science and technology
3. Australia and COVID-19
4. Data science context
5. Tasmania COVID-19 spread: systems dynamic modeling
6. North West Tasmania emergency response and recovery dynamic scenario modeling
7. Conclusions and future work
20. Essentials of COVID-19 and treatment approaches
1. Introduction
2. History and epidemiology
3. Structure of SARS-CoV-2
4. Pathogenesis
5. Treatment approaches
6. Various approaches to design vaccine
7. Conclusions
21. Coronavirus epidemic and its social/mental dimensions: the Turkey case
1. Introduction
2. Looting culture and perspectives with phenomenology
3. Unethical criminal and the “produced anxiety”®
4. Self-ostracism® and the coronavirus hallucination
5. Political structure of the coronavirus and the cultural racism
6. Conclusion: Foucault, “big locking down,” and “the world is a great madhouse”
22. Coronavirus: a scientometric study of worldwide research publications
1. Introduction
2. Methodology
3. Analysis and results
4. Results and Findings
5. Conclusion
23. The effects of COVID-19 pandemic on Western Balkan financial markets
1. Introduction
2. Literature review
3. What is coronavirus
4. The effects of globalization
5. Materials and methods
6. Discussion
7. Conclusion
Appendix
24. Prioritization of health emergency research and disaster preparedness: a systematic assessment of the coronavirus disease 2019 pandemic
1. Introduction
2. Data analytics
3. Evaluation of collaboration network in health emergency research
4. Analytic platforms
5. Results
6. Discussion
7. Conclusion
Appendix A. Supplementary data
25. A review on epidemiology, genomic characteristics, spread, and treatments of COVID-19
1. Introduction
2. Origin of coronavirus
3. Epidemiology of coronavirus
4. Transmission
5. Genome structure
6. Phylogenetic analysis
7. Statistical analysis on COVID-19
8. Clinical features of COVID-19
9. Treatment with drugs
10. Progress on vaccines
11. Conclusions
26. Control of antibiotic resistance and superinfections as a strategy to manage COVID-19 deaths
1. Introduction
2. Effect of superinfections on immune response and the severity of COVID-19 infection
3. Antibiotic resistance as a challenge in the COVID-19 pandemic
4. Alternative treatment strategies to overcome antibiotic resistance as a contributor to COVID-19 deaths
5. Mouthwashes as an early preventive strategy
27. Assessment of global research trends in the application of data science and deep and machine learning to the COVID-19 pandemic
1. Introduction
2. Data and methodology
3. Results and discussion
4. Conclusion
28. Identification of lead inhibitors of TMPRSS2 isoform 1 of SARS-CoV-2 target using neural network, random forest, and molecular docking
1. Introduction
2. Materials and methods
3. Result and discussion
29. The linkage between the epidemic of COVID-19 and oil prices: case of Saudi Arabia, January 22 to April 17
1. Introduction
2. Materials and methods
3. Coronavirus: pandemic scaring the world
4. Empirical results
5. Conclusion and recommendation
30. Role of big geospatial data in the COVID-19 crisis
1. Introduction
2. Materials and methods
3. Background
4. GeoAI: geospatial artificial intelligence and health geographics
5. Big geospatial data and infectious disease pattern
6. Case studies of China and Taiwan
7. Results and discussions
8. Conclusion
31. COVID-19: will it be a game changer in higher education in India?
1. Introduction
2. Proposed basic learning model
3. COVID-19: challenges in Indian education
4. Proposed simulation
5. Analysis of results
6. Conclusion
32. Are the northern and southern regions equally affected by the COVID-19 pandemic? An empirical evidence from Nigeria
1. Introduction
2. Materials and methods
3. Results and discussion
4. Conclusion and recommendation
33. COVID-19 lethality reduction using artificial intelligence solutions derived from telecommunications systems
1. Introduction
2. Literary review
3. European Telecommunications Standards Institute architecture
4. Data required to reduce COVID lethality
5. Adaptation of the European Telecommunications Standards Institute architecture to the needs of public services
6. Conclusion
34. The significance of daily incidence and mortality cases due to COVID-19 in some African countries
1. Introduction
2. Literature review
3. Materials and methods
4. Results and discussion
5. Conclusion
35. Data interpretation leading to image processing: a hybrid perspective to a global pandemic, COVID-19
1. Introduction
2. Literature review
3. Data analysis
4. Case study: COVID-19
5. Image processing analysis
6. Results and discussion
7. Conclusion and future work
Appendix A: Country-wise coronavirus details
36. COVID-19: in the direction of monitoring the pandemic in India
1. Introduction
2. Origin and mode of transmission of COVID-19
3. Clinical characteristics of COVID-19
4. Current scenario of COVID-19 in India
5. Precautions taken in India for controlling COVID-19 spread
6. Problems faced in controlling COVID-19 spread
7. Significance and outcomes of the adopted measures
8. Future measures
9. Conclusion
37. Potential antiviral therapies for coronavirus disease 2019 (COVID-19)
1. Introduction
2. Genome organization
3. Epidemiology and clinical features
4. Potential antiviral therapies under consideration
5. Conclusion and future work
Index

Utku Kose

Dr. Utku Kose is an Associate Professor at Süleyman Demirel University, Turkey. He received his PhD from Selcuk University, Turkey, in the field of computer engineering. He has more than 100 publications to his credit, including Deep Learning for Medical Decision Support Systems, Springer; Artificial Intelligence Applications in Distance Education, IGI Global; Smart Applications with Advanced Machine Learning and Human-Centered Problem Design, Springer; Artificial Intelligence for Data-Driven Medical Diagnosis, DeGruyter; Computational Intelligence in Software Modeling, DeGruyter; Data Science for Covid-19, Volumes 1 and 2, Elsevier/Academic Press; and Deep Learning for Medical Applications with Unique Data, Elsevier/Academic Press, among others. Dr. Kose is a Series Editor of the Biomedical and Robotics Healthcare series from Taylor & Francis/CRC Press. His research interests include artificial intelligence, machine ethics, artificial intelligence safety, optimization, chaos theory, distance education, e-learning, computer education, and computer science.

Affiliations and expertise

Associate Professor, Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey

Deepak Gupta

Dr. Deepak Gupta is an assistant professor at Maharaja Agrasen Institute of Technology, Delhi, India. He is an eminent academician, including roles as lecturer, researcher, consultant, community service, PhD, and post-doctorate supervision. Dr. Gupta focuses on rational and practical learning and has contributed important literature in the fields of Human-Computer Interaction, Intelligent Data Analysis, Nature-Inspired Computing, Machine Learning, and Soft Computing. Dr. Gupta has authored/edited a number of books, including Emerging Trends and Roles of Fog, Edge, and Pervasive Computing in Intelligent IoT-Driven Applications, Wiley; Advanced Machine Intelligence and Signal Processing, Springer; Deep Learning for Medical Applications with Unique Data, Elsevier/Academic Press; Explainable Edge AI: A Futuristic Computing Perspective, Springer; Applications of Big Data in Healthcare, Elsevier/Academic Press; and Data Science for Covid-19, Volumes 1 and 2, Elsevier/Academic Press; among others.

Affiliations and expertise

Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India

Victor Hugo Costa de Albuquerque

Victor Hugo C. de Albuquerque [M’17, SM’19] is a collaborator Professor and senior researcher at the Graduate Program on Teleinformatics Engineering at the Federal University of Ceará, Brazil, and at the Graduate Program on Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza/CE, Brazil. He has a Ph.D in Mechanical Engineering from the Federal University of Paraíba (UFPB, 2010), an MSc in Teleinformatics Engineering from the Federal University of Ceará (UFC, 2007), and he graduated in Mechatronics Engineering at the Federal Center of Technological Education of Ceará (CEFETCE, 2006). He is a specialist, mainly, in Image Data Science, IoT, Machine/Deep Learning, Pattern Recognition, Robotic.

Affiliations and expertise

Professor and Senior Researcher, Federal University of Ceara, Fortaleza, Graduate Program on Teleinformatics Engineering,Fortaleza/CE, Brazil

Ashish Khanna

Dr. Ashish Khanna has 16 years of expertise in teaching, entrepreneurship, and research and development. He received his PhD from the National Institute of Technology, Kurukshetra, India, and completed a post-doc degree at the National Institute of Telecommunications (Inatel), Brazil. He has published around 40 SCI-indexed papers in 'IEEE Transactions', and in other reputed journals by Springer, Elsevier, and Wiley, with a cumulative impact factor of above 100. He has published around 90 research articles in top SCI/Scopus journals, conferences, and book chapters. He is co-author or editor of numerous books, including 'Advanced Computational Techniques for Virtual Reality in Healthcare' (Springer), 'Intelligent Data Analysis: From Data Gathering to Data Comprehension' (Wiley), and 'Hybrid Computational Intelligence: Challenges and Applications' (Elsevier). His research interests include distributed systems, MANET, FANET, VANET, Internet of Things, and machine learning. He is one of the founders of Bhavya Publications and the Universal Innovator Lab, which is actively involved in research, innovation, conferences, start-up funding events, and workshops. He is currently working at the Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India, and is also a Visiting Professor at the University of Valladolid, Spain.

Affiliations and expertise

Sr. Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology (MAIT), New Delhi, India