Lessons from Covid-19: Impact on Healthcare Systems and Technology

Lessons from COVID-19: Impact on Healthcare Systems and Technology uncovers the impact that COVID-19 has made on healthcare and technology industries. State-of-the-art case studies, empirical research, and new trends in technology-mediated solution are discussed to help inform and guide readers in understanding the effects that the COVID-19 outbreak has had across healthcare and technology industries. The book discusses challenges to identify vaccines, changes in legislation on clinical trials and re-purposing of licensed drugs, effects on primary healthcare, best practices adopted by different countries to control the pandemic, and different effects on patients within diverse age groups and comorbidities.

In addition, the book covers technology-mediated solutions and infrastructures applied, digital transformations, modeling techniques, statistical projections, and the benefits and use of cloud computing and artificial intelligence. This is a valuable resource for healthcare professionals, medical doctors, researchers and graduate students from both biomedical and technological fields who are interested in learning more about the use of new technologies to fight a pandemic.

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. Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), a Not-for-Profit Scientific Network for Innovation and Research Excellence connecting Industry and Academia. The Network with HQ in Seattle, USA has currently more than 1,500 scientific members from over 105 countries. As an Investigator / Co-Investigator, he has won research grants worth over 100+ Million US$. Currently he works as a Professor of Artificial Intelligence in Innopolis University, Russia and is a Chairholder of the Yayasan Tun Ismail Mohamed Ali Professorial Chair in Artificial Intelligence of UCSI, Malaysia. Dr. Abraham works in a multi-disciplinary environment and he has authored / coauthored more than 1,400+ research publications out of which there are 100+ books covering various aspects of Computer Science. One of his books was translated to Japanese and few other articles were translated to Russian and Chinese. Dr. Abraham has more than 45,500+ academic citations (h-index of 100 as per google scholar). He has given more than 150 plenary lectures and conference tutorials (in 20+ countries). Since 2008, Dr. Abraham was the Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (which has over 200+ members) during 2008-2021 and served as a Distinguished Lecturer of IEEE Computer Society representing Europe (2011-2013). Dr. Abraham was the editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) during 2016-2021 and is currently serving / served the editorial board of over 15 International Journals indexed by Thomson ISI. Dr. Abraham received Ph.D. degree in Computer Science from Monash University, Melbourne, Australia (2001) and a Master of Science Degree from Nanyang Technological University, Singapore (1998).

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.