Table of Contents

PART I: Healthcare Sector1. History of COVID-192. Problems and challenges in identifying vaccines for COVID-193. Changes of legislation on clinical trials, and re-purposing of licensed drugs for the treatment of COVID-19 symptoms4. Effects of COVID-19 on primary health care5. Impact of COVID-19 on expectant parents and different stakeholders6. Effects of COVID-19 on doctors, medical professionals, nurses and supporting staffs of hospitals7. Best practises adopted by different countries to control the COVID-19 pandemic8. Disproportionate healthcare implications of COVID-19 due to age, ethnicity, gender, and socio-economic status9. How COVID-19 pandemic has affected Global healthcare and its implications for the future of Healthcare10. Socio-cultural and Economic effects of COVID-19 on global health care11. Psychological effects of COVID-19 on human beings12. Effects of COVID-19 on medical and clinical researchers13. Effects of COVID-19 on Tele-medicine, Clinical workflow, Security and Privacy Issues14. Effects of COVID-19 on drug manufacturing companies.15. Effects of COVID-19 on kids 16. Effects of COVID-19 on the middle age and 21st century generation.17. Effects of COVID-19 on the healthcare of the elderly18. Effects of COVID-19 for people with diseases like Cancer, HIV, Mental health patients, and people with disabilities in general19. Health and safety best practices for use in Health environments and Public spaces during and after COVID-19

PART II: Computing and Informatics20. Future research scope on COVID-19 treatment and Health informatics21. COVID-19 Technology-mediated solutions and infrastructures22. e-Health and Post-COVID-19 era23. Role and impact of digital technologies and mass media during COVID-1924. Digital transformation of the healthcare sector triggered by the Covid-19 pandemic25. Advances in virtual clinical learning, diagnosis, and therapy26. Modelling techniques and statistical projections that are used to predict the spread of the COVID-19 virus and decision making27. Success rates and turnaround times with COVID-19 using ICT28. Main enabling ICT technologies for the development of cutting-edge healthcare systems and services29. Effects of COVID-19 on virtual healthcare with the usage of Internet of Things (IoT) technology30. Benefits and use of Cloud computing to provide IT infrastructure during COVID-1931. Benefits and use of Edge computing to provide IT infrastructure during COVID-1932. Benefits and use of Artificial Intelligence (AI) to provide IT infrastructure during COVID-1933. Benefits and use of Big Data Management and Machine Learning techniques to provide IT infrastructure during COVID-19

About the Author

A. Kaklauskas is a professor at Vilnius Gediminas Technical University, in Lithuania; Member of the Research Council of Lithuania; Member of the Science Europe working group on Data Sharing and Supporting Infrastructures; Head of the Department of Construction Management and Property; member of the Lithuanian Academy of Sciences; editor-in-chief of Journal of Civil Engineering and Management; editor of Engineering Applications of Artificial Intelligence; and associate editor of Ecological Indicators. He contributed to nine Framework and five Horizon 2020 program projects and participated in over 30 other projects in the EU, US, Africa. and Asia. His publications include nine books and 150 papers. His areas of interest include affective computing; intelligent tutoring systems; affective intelligent tutoring systems; massive open online courses (MOOCS); affective internet of things; smart built environment; intelligent event prediction; opinion mining; intelligent decision support systems; life cycle analyses of built environments; big data and text analytics. Dr. Ajith Abraham is a Pro Vice-Chancellor at Bennette University. He is the director of Machine Intelligence Research Labs (MIR Labs), Australia. MIR Labs are a not-for-profit scientific network for innovation and research excellence connecting industry and academia. His research focuses on real world problems in the fields of machine intelligence, cyber-physical systems, Internet of things, network security, sensor networks, Web intelligence, Web services, and data mining. He is the Chair of the IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing. He is editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) and serves on the editorial board of several international journals. He received his PhD in Computer Science from Monash University, Melbourne, Australia.

Kingsley Okoye received his PhD in Software Engineering from the University of East London, UK. He is an MIET member at the Institution of Engineering and Technology, UK, and a Graduate Member of the IEEE. He is a devoted researcher to Industry and Academia in areas such as Data Science, Machine Learning, Artificial Intelligence, Big Data and Advanced Analytics, Software Development and Programming, and Business Process Management. Kingsley is a Data Architect in the Writing Lab of Tecnologico de Monterrey. He is also a member of the Machine Intelligence Research Labs, USA, and a member of the IEEE SMCS Technical Committee (TC) on Soft Computing. His Research interests includes: Process Mining and Automation, Learning Analytics and Systems Design, Semantic Web Technologies, Knowledge Engineering and Data Management, Computer Education, Educational Innovation, Internet Applications and Ontologies. Shankru Guggari is a PhD in computer science and engineering currently working on building robust classification techniques. His areas of interest are in recognition, IOT, and machine learning. Dr. Guggari has published research works in international conferences and reputed journals such as Elsevier and Springer, to name a few, and has more than four years industry and two years academic research experience. He is currently as serving as an editor for two edited books (Elsevier, Taylor, and Francis publishers). He has delivered various technical talks in different platforms. He also served as guest editor for few special issues related to AI and ML domains.