Healthcare Informatics for Fighting COVID-19 and Future Epidemics

Introduction.-  SECTION-1: Health Data Analytics and Mining for the COVID-19 pandemic.- COVID-19 pandemic big data analytics and application.- Artificial Intelligence approaches for the COVID-19 pandemic.- Machine learning and deep learning for the COVID-19 pandemic.- Cyber-Social Data Processing and Intelligence Mining for the COVID-19 pandemic.- Cloud-based Intelligent systems for the COVID-19 pandemic.- Smart hospital requirements for infectious diseases treatment.- Big data-driven health risk identification.- Pattern recognition in epidemic risk analysis.- Predictive modelling for the COVID-19 pandemic and future epidemics.- Image processing and computer vision for the COVID-19 pandemic.- Sentiment analysis for the COVID-19 pandemic.- Patient behaviour modelling for the COVID-19 pandemic.- Decision Support Systems (DSS) for the COVID-19 pandemic.- Disease outbreak and progression modelling and simulation for COVID-19 pandemic.- SECTION-2: Information exchange, knowledge sharing, data storage, and security for the COVID-19 pandemic.- COVID-19 information exchange.- Knowledge-sharing for the COVID-19 pandemic.- Blockchain for secured COVID-19 pandemic data handling.- Ontology-based models for the COVID-19 pandemic.- Cloud storage of the COVID-19 pandemic data.- Data warehousing for the COVID-19 pandemic.- Privacy and ethical issues for the COVID-19 pandemic information exchange and sharing.- Text mining and natural language processing for the COVID-19 pandemic.- Secure communication of the COVID-19 pandemic data.- Ensuring the integrity and reliability of the COVID-19 pandemic information.- Infodemic and fake news detection and its social spread prevention for the COVID-19 pandemic.- Data integrity, consistency, and compliance for the COVID-19 pandemic.- Health information impact assessment for the COVID-19 pandemic.- Secure handling and exchange of patient-generated data for the COVID-19 pandemic.- SECTION-3: The Internet of things (IoT) and the Internet

Lalit Garg is an Information Technology innovator, academician, learner, teacher, developer, keynote speaker, Consultant, and researcher with a demonstrated history of working in the higher education industry and an excellent research publication record. He is skilled in solving complex problems using machine learning and data analytics, especially from the medicine and healthcare domain. Presently he is a Senior Lecturer in Computer Information Systems at the University of Malta, Msida, Malta. He is also an honorary lecturer at the University of Liverpool, UK. He has also worked as a researcher at the Nanyang Technological University, Singapore and the University of Ulster, UK. He has supervised more than 180 Masters' dissertations and guiding 4 PhD thesis. He is awarded a research studentship in Healthcare Modelling to carry out his PhD research studies in the faculty of computing and engineering at the University of Ulster, UK. His PhD research was nominated for the Operational Research Society Doctoral Prize "Most Distinguished Body of Research leading to the Award of a Doctorate in the field of OR". He participates in many EU-funded, and local funded projects, including a one million euros Erasmus+ Programme Capacity-Building projects in the field of Higher Education (CBHE) titled Training for Medical education via innovative eTechnology (MediTec). The University of Malta has awarded him the 2021-22 Research Excellence Fund for exploring Novel Intelligent Computing Methods for healthcare requirements forecasting, allocation and management (NICE-Healthcare).

 

Dr Chinmay Chakraborty is an Assistant Professor at the Department of Electronics and Communication Engineering, BIT Mesra. His primary research areas include wireless body area networks, the Internet of Medical Things, energy-efficient wireless communications and networking, and point-of-care diagnosis. He received an Outstanding Researcher Award from TESFA