Epidemic Analytics for Decision Supports in COVID19 Crisis (eBook, PDF)

Redaktion: Marques, Joao Alexandre Lobo; Fong, Simon James

• Format: PDF

Produktbeschreibung

Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations.

Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 158
• Erscheinungstermin: 20. Mai 2022
• Englisch
• ISBN-13: 9783030952815
• Artikelnr.: 64082593

Autorenporträt

Joao Alexandre Lobo Marques is an Associate Professor, Head of Department and Research Coordinator at the University of Saint Joseph, Macau, SAR China and Visiting Associate Professor at the Shenzhen Institutes of Advanced Technology/Chinese Academy of Sciences - SIAT/CAS. Post Doctorate and Honorary Visiting Fellow from the University of Leicester-UK. Founder of the Laboratory of Applied Neurosciences LAN/USJ. Received his PhD in Biomedical Engineering at UFC/Brazil in 2010 and the MSc degree in Engineering of Teleinformatics from UFC in 2007. He is also a Board Member of the XS Innovation Group in Brazil. Worked as a Researcher and Innovation Director at Centrovita Medical Center (Angola). Developed a solid international career with academic positions and relevant research projects developed in Asia (China), Europe (England, Germany and Portugal), Africa (Angola), and America (United States and Brazil). More than 70 papers published in high impact international journals and relevant conferences. His research interests include heart signal processing (EKG, HRV, QTV, EEG and others), digital image processing, biofeedback, applied neurosciences, telemedicine, computational and artificial intelligence, machine learning and deep learning, mathematical transforms, nonlinear analysis of biological time series. Simon James Fong graduated from La Trobe University in Australia, with a First Class Honours BEng. Computer Systems degree and a PhD. Computer Science degree in 1993 and 1998 respectively. Simon is now working as an Associate Professor in the Computer and Information Science Department of the University of Macau. He is also one of the founding members of the Data Analytics and Collaborative Computing Research Group in the Faculty of Science and Technology. Prior to joining the University of Macau, he worked as an Assistant Professor in the School of Computer Engineering at Nanyang Technological University in Singapore. Before his academic career, Simon took up various managerial and engineering posts, such as being a systems engineer, IT consultant, integrated network specialist, and e-commerce director in Melbourne, Hong Kong. and Singapore. Some companies that he worked at before include Hong Kong Telecom, Singapore Network Services, AES Pro-Data, and the United Overseas Bank in Singapore. Dr. Fong has published over 350 peer-reviewed international conference and journal papers, mostly in the area of e-Commerce technology and data-mining. Actively, Dr. Fong has served as General Chair for several major international conferences and workshops in recent years.

Inhaltsangabe

Chapter 1. Research and Technology Development Achievements During the COVID-19 Pandemic - An Overview.- Chapter 2. Analysis of the COVID-19 Pandemic Behavior based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models.- Chapter 3. The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak.- Chapter 4. Probabilistic Forecasting Model for the COVID-19 Pandemic based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System.- Chapter 5. The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID-19 Pandemic.- Chapter 6. A Quantum Field formulation for a pandemic propagation.