Data Science for COVID-19

Data Science for COVID-19

Volume 2: Societal and Medical Perspectives

1st Edition - October 22, 2021

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  • Editors: Utku Kose, Deepak Gupta, Victor de Albuquerque, Ashish Khanna
  • Paperback ISBN: 9780323907699
  • eBook ISBN: 9780323907705

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Description

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.

Key Features

  • Provides a leading-edge survey of Data Science techniques and methods for research, mitigation and the treatment of the COVID-19 virus
  • Integrates various Data Science techniques to provide a resource for COVID-19 researchers and clinicians around the world, including the wide variety of impacts the virus is having on societies and medical practice
  • Presents insights into innovative, data-oriented modeling and predictive techniques from COVID-19 researchers around the world, including geoprocessing and tracking, lab data analysis, and theoretical views on a variety of technical applications
  • Includes real-world feedback and user experiences from physicians and medical staff from around the world for medical treatment perspectives, public safety policies and impacts, sociological and psychological perspectives, the effects of COVID-19 in agriculture, economies, and education, and insights on future pandemics

Readership

Academics (scientists, researchers, MSc. PhD. students) from the fields of Computer Science and Engineering, Biomedical Engineering, Biology, Chemistry, Electronics and Communication Engineering, and Information Technology. The audience also includes interested professionals-experts from both public and private industries of medical, computer, data science, information technologies. Data Science, Medical, Biomedical, Artificial Intelligence, Machine Learning, Deep Learning, and even Data (i.e. Image, Signal) Processing oriented courses given at especially Health, Biology, Biomedical Engineering or similar programs of universities, institutions

Table of Contents

  • 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

Product details

  • No. of pages: 812
  • Language: English
  • Copyright: © Academic Press 2021
  • Published: October 22, 2021
  • Imprint: Academic Press
  • Paperback ISBN: 9780323907699
  • eBook ISBN: 9780323907705

About the Editors

Utku Kose

Dr. Utku Kose is an Associate Professor at Su¨leyman 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 articles, authored and edited books, proceedings, and reports. He is also a series editor of the 'Biomedical and Robotics Healthcare' (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 eminent academician; plays versatile roles and responsibilities juggling between lectures, research, publications, consultancy, community service, PhD and post-doctorate supervision. With 12 years of rich expertise in teaching and two years in industry; he focuses on rational and practical learning. He has contributed important literature in the fields of Human-Computer Interaction, Intelligent Data Analysis, Nature-Inspired Computing, Machine Learning and Soft Computing. He is an Assistant Professor at Maharaja Agrasen Institute of Technology, Delhi, India. He has completed his Post-Doc from Inatel, Brazil, and Ph.D. from Dr. APJ Abdul Kalam Technical University. He has served as Editor-in-Chief, Guest Editor, and Associate Editor in SCI and various other reputed journals (Elsevier, Springer, Wiley & MDPI). He is currently a Post-Doc researcher at University of Valladolid, Spain. He has authored/edited a number of books, including Advanced Computational Techniques for Virtual Reality in Healthcare and Handbook of Computer Networks and Cyber Security: Principles and Paradigms from Springer, He has published 101 scientific research publications in reputed International Journals and Conferences including 49 SCI Indexed Journals of IEEE, Elsevier, Springer, Wiley and many more. He has also published one patent. He is Editor-in-Chief of Computers and Quantum Computing and Applications journal, Associate Editor of Expert Systems (Wiley), Intelligent Decision Technologies (IOS Press), Journal of Computational and Theoretical Nenoscience, and Honorary Editor of ICSES Transactions on Image Processing and Pattern Recognition. He is also a series editor of Intelligent Biomedical Data Analysis, De Gruyter (Germany).

Affiliations and Expertise

Assistant Professor, Maharaja Agrasen Institute of Technology, Guru Gobind Singh Indraprastha University, Delhi, India

Victor 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

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