Leveraging Artificial Intelligence in Global Epidemics
- 1st Edition - July 28, 2021
- Editors: Le Gruenwald, Sarika Jain, Sven Groppe
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 8 9 7 7 7 - 8
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 0 3 8 2 - 0
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 0 0 0 2 - 7
Leveraging Artificial Intelligence in Global Epidemics provides readers with a detailed technical description of the role Artificial Intelligence plays in various stages of a dise… Read more
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Request a sales quoteLeveraging Artificial Intelligence in Global Epidemics provides readers with a detailed technical description of the role Artificial Intelligence plays in various stages of a disease outbreak, using COVID-19 as a case study. In the fight against epidemics, medical staff are on the front line; but behind the lines the battle is fought by researchers, and data scientists. Artificial Intelligence has been helping researchers with computer modeling and simulation for predictions about disease progression, the overall economic situation, tax incomes and population development. In the same manner, AI can prepare researchers for any emergency situation by backing the medical science. Artificial Intelligence plays a key and cutting-edge role in the preparedness for and dealing with the outbreak of global epidemics. It can help researchers analyze global data about known viruses to predict the patterns of the next pandemic and the impacts it will have. Not only prediction, AI plays an increasingly important role in assessing readiness, early detection, identification of patients, generating recommendations, situation awareness and more. It is up to the right input and the innovative ways by humans to leverage what AI can do. As COVID-19 has grabbed the world and its economy today, an analysis of the COVID-19 outbreak and the global responses and analytics will pay a long way in preparing humanity for such future situations.
- Provides readers with understanding of how Artificial Intelligence can be applied to the prediction, forecasting, detection, and testing of global epidemics, using COVID-19 and other recent epidemics such as Ebola, Corona viruses, Zika, influenza, Dengue, Chikungaya, and malaria as case studies
- Includes background material regarding readiness for coping with epidemics, including Machine Learning models for prediction of epidemic outbreaks based on existing data
- Includes technical coverage of key topics such as generating recommendations to combat outbreaks, genome sequencing, AI-assisted testing, AI-assisted contact tracing, situation awareness and combating disinformation, and the role of Artificial Intelligence and Machine Learning in drug discovery, vaccine development, and drug re-purposing
Academics (scientists, researchers, MSc. PhD. students) from the fields of Computer Science, Biology, and healthcare practitioners. The audience includes researchers and practitioners in any field that deals with epidemics research. Courses in Artificial Intelligence, Machine Learning, and Data Mining
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- About the editors
- About the authors
- Preface
- Target audience
- Key features and strengths
- A quick guide to reading the book
- 1. An overview of global epidemics and the challenges faced
- Abstract
- Key points
- 1.1 Introduction to global epidemics
- 1.2 List of epidemics
- 1.3 Origin of epidemics hitting the globe
- 1.4 Comparison of the magnitude of all epidemics
- 1.5 Assessing countries’ readiness for coping with epidemics
- 1.6 Challenges in battling with epidemics
- 1.7 Concerns about future pandemics
- 1.8 Preparative measures for tackling future pandemics
- 1.9 Review questions
- 1.10 Problem statements for young researchers
- 1.11 Discussion questions
- References
- 2. Leveraging artificial intelligence and digital tech to help citizens, societies, and economies survive and strive during pandemics
- Abstract
- Key points
- 2.1 Introduction
- 2.2 Key levers to improve handling of pandemic events
- 2.3 The situation today
- 2.4 Healthwise and economic impacts of Pandemic Cohort Management
- 2.5 Counteracting economic and societal side effects
- 2.6 Conclusion: toward resilient societies
- 2.7 Questions for review
- 2.8 Questions for discussion
- 2.9 Problem statements for young researchers
- References
- 3. Towards an alternative to lockdown: Pandemic management leveraging digital technologies and artificial intelligence
- Abstract
- Key points
- 3.1 Towards an alternative to lockdown: Pandemic management leveraging digital technologies and artificial intelligence
- 3.2 Conclusion
- Review questions
- Discussion questions
- Problem statements for young researchers
- References
- 4. Exploratory study of existing approaches for analyzing epidemics
- Abstract
- Key points the chapter describes
- 4.1 Introduction
- 4.2 Pandemics dataset repositories
- 4.3 Analysis of pandemics using visualization
- 4.4 Epidemic modeling for analysis and prediction of pandemics
- 4.5 Machine learning approaches for analysis and prediction of pandemics
- 4.6 Semantic technologies
- 4.7 Summary
- Review questions
- Discussion questions
- Problem statement
- References
- 5. A data science perspective of real-world COVID-19 databases
- Abstract
- Key points
- 5.1 Introduction
- 5.2 Publicly available data sources for COVID-19: an overview
- 5.3 Cerner real-world data
- 5.4 Machine learning
- 5.5 Classification
- 5.6 Review questions
- 5.7 Discussion questions
- 5.8 Problem statements
- 5.9 Conclusion
- Acknowledgments
- References
- 6. Preparing with predictions: forecasting epidemics with artificial intelligence
- Abstract
- Keypoints
- 6.1 Introduction
- 6.2 The data that powers pandemic predictions
- 6.3 Pandemic applications of predictive models
- 6.4 Conclusion
- Review questions
- Discussion questions
- Problem statements for young researchers
- References
- 7. The worldwide methods of artificial intelligence for detection and diagnosis of COVID-19
- Abstract
- Key points
- 7.1 Introduction
- 7.2 Artificial intelligence for virus detection and diagnosis
- 7.3 Artificial intelligence for detection and diagnosis of COVID-19
- 7.4 Concluding remarks and future trends and concerns
- Review questions
- Discussion questions
- Problem statements
- References
- 8. The role of AI in digital contact tracing
- Abstract
- Key points
- 8.1 Introduction
- 8.2 The essence of digital contact tracing
- 8.3 Proximity-based DCT
- 8.4 Location-based DCT
- 8.5 DCT beyond the apps
- 8.6 Conclusion
- Review questions
- Discussion questions:
- Problem Statements
- References
- 9. Covid-19 accelerating the dynamics of Artificial Intelligence disruption
- Abstract
- Key points
- 9.1 Introduction
- 9.2 Artificial intelligence automation in COVID-19 times
- 9.3 The limits of Artificial Intelligence
- 9.4 Artificial Intelligence’s impact on work and digitally transformed firms
- 9.5 The basis for Artificial Intelligence-augmented and digitally transformed firms’ operational advantages
- 9.6 Dynamics at macro and micro level
- Review questions
- Discussion questions
- Problem statements
- References
- Further reading
- 10. Use of artificial intelligence in pharmacovigilance for social media network
- Abstract
- Key points
- 10.1 Introduction
- 10.2 A brief on artificial intelligence
- 10.3 Pharmacovigilance
- 10.4 Methodology and analysis
- 10.5 Discussion
- 10.6 Review questions
- 10.7 Discussion questions
- 10.8 Conclusion
- Problem statement
- References
- Further reading
- 11. System-level knowledge representation for artificial intelligence during pandemics
- Abstract
- Key points
- 11.1 Pandemics as disasters
- 11.2 Artificial intelligence and sociotechnical complexity
- 11.3 System-level knowledge representation
- 11.4 Information standards and initiatives
- 11.5 Two cases Taiwan and Singapore
- 11.6 What is intelligence, again? Remind me
- 11.7 The future of artificial intelligence during pandemics
- Review questions
- Discussion questions
- Problem statements
- References
- Index
- No. of pages: 316
- Language: English
- Edition: 1
- Published: July 28, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780323897778
- eBook ISBN: 9780323903820
- eBook ISBN: 9780323900027
LG
Le Gruenwald
Dr. Le Gruenwald is a Professor, Dr. David W. Franke Professor, and Samuel Roberts Noble Foundation Presidential Professor in the School of Computer Science at The University of Oklahoma. She received her Ph.D. in Computer Science from Southern Methodist University. She worked for National Science Foundation (NSF) as a Cluster Lead and Program Director of the Information Integration and Informatics program and a Program Director of the Cyber Trust program; for NEC America, Advanced Switching Laboratory as a Member of the Technical Staff in the Database Management Group; for Southern Methodist University as a Lecturer in the Computer Science and Engineering Department; and for WRT as a Software Engineer.
Affiliations and expertise
Professor, School of Computer Science, The University of Oklahoma, Norman, Oklahoma, USASJ
Sarika Jain
Dr. Sarika Jain graduated from Jawaharlal Nehru University (India) in 2001. Her doctorate is in the field of Knowledge Representation in Artificial Intelligence which was awarded in 2011. She has served in the field of education for over 19 years and is currently working at the National Institute of Technology Kurukshetra (Institute of National Importance), India. Dr. Jain has authored / co-authored over 100 publications including books. Her current research interests include Knowledge Management and Analytics; Semantic Web; Ontological Engineering; and Intelligent Systems.
Dr. Jain has supervised two doctoral scholars (5 ongoing) who are now pursuing their post doctorates, one in Spain and the other in Germany. Currently, she is guiding 15 students for their Master’s and Doctoral research work in the area of Knowledge Representation. She is serving as a reviewer for Journals of IEEE, Elsevier, and Springer. She has been involved as a program and steering committee member in many prestigious conferences in India and abroad.
She has two research-funded projects: one ongoing project is funded by CRIS TEQUIP-III worth Rs 2.58 lakhs, and the other completed project is funded by DRDO, India worth Rs 40 lakhs. She has also applied for a patent in Nov 2019. Dr. Jain has held various administrative positions at department as well as at institute level in her career like HOD, Hostel Warden, Faculty Incharge of technical and cultural fests, member of Research Degree Committee, and Center Incharge Examinations.
Dr. Jain has visited the United Kingdom and Singapore for presenting her research work. She has constantly been supervising DAAD interns from different German universities and many interns from India every summer. She works in collaboration with various researchers across the globe including Germany, Austria, Australia, Malaysia, the United States, Romania and many others. She has organized various challenges, conferences and workshops including NITC, GIAN by MHRD, ISIC, ICSCC, ICACCT, ICECCS, and EWAD. She is a member of IEEE and ACM and a Life Member of Computer Society of India (CSI), International Association of Engineers (IAENG), and the International Association of Computer Science and Information Technology (IACSIT).
Dr. Jain is highly interested in world-wide collaborations and seeking scholars and interns in her research group.
Affiliations and expertise
Assistant Professor, National Institute of Technology, Kurukshetra, Haryana, IndiaSG
Sven Groppe
Dr. Sven Groppe is a Lecturer and Tutor at the University of Lübeck, Germany. His publication record contains over 100 publications, including the book Data Management and Query Processing in Semantic Web Databases, published by Springer. He was a member of the DAWG W3C Working Group, which developed SPARQL. He was the project leader of the DFG project LUPOSDATE, an open-source Semantic Web database, and of two research projects in the area of FPGA acceleration of relational and Semantic Web databases. He is also leading a DFG project on GPU and APU acceleration of main-memory database indexes, and a DFG project about Semantic Internet of Things. He is also the chair of the Semantic Big Data workshop series, which is affiliated with the ACM SIGMOD conference (so far 2016 to 2020), and of the Very Large Internet of Things workshop in conjunction with the VLDB conference (so far 2017 to 2020). He is general chair of the upcoming International Semantic Intelligence Conference (ISIC) in 2021. His research interests include artificial intelligence, databases, Semantic Web, query and rule processing and optimization, Cloud Computing, acceleration via GPUs and FPGAs, peer-to-peer (P2P) networks, Internet of Things, data visualization and visual query languages.
Affiliations and expertise
Lecturer, University of Lubeck, Lubeck, Schleswig-Holstein, GermanyRead Leveraging Artificial Intelligence in Global Epidemics on ScienceDirect