The contributions in this carefully curated volume, present cutting-edge research in applied mathematical modeling for combating COVID-19 and other potential pandemics. Mathematical modeling and intelligent control have emerged as powerful computational models and have shown significant success in combating any pandemic. These models can be used to understand how COVID-19 or other pandemics can spread, analyze data on the incidence of infectious diseases, and predict possible future scenarios concerning pandemics. This book also discusses new models, practical solutions, and technological advances related to detecting and analyzing COVID-19 and other pandemics based on intelligent control systems that assist decision-makers, managers, professionals, and researchers. Much of the book focuses on preparing the scientific community for the next pandemic, particularly the application of mathematical modeling and intelligent control for combating the Monkeypox virus and Langya Henipavirus.

Overview of state of art and general concepts of mathematical models and intelligent control systems Mathematical models such as SIR and SER models and optimal control for combating COVID-19 pandemic Original research on intelligent control techniques such as Fuzzy logic, Machine Learning and Deep Learning

Autorentext
Zakia Hammouch is currently a full Professor at the Ecole Normale Supérieure of the University Moulay Ismail Meknès. She is received her Masters in Applied Mathematics and her PhD in Numerical Analysis and Fluid Mechanics from the University of Picardie Jules Verne Amiens France. She is Researcher at the Division of Applied Mathematics of Thu Dau Mot University Binh Duong in Vietnam and Consultant at the Department of Medical Research, China Medical University Hospital, Taichung, Taiwan. She is a Member of the European Women in Mathematics (EWM) Association, a Permanent Member of the Organization for Women in Science for the Developing World (OWSD) and an Advisory Member of the Abdus Salam School of Mathematical Sciences, Pakistan. She has published more than 100 articles and chapters in indexed journals and reputable books (Springer, Elsevier,...). She is a member of editorial boards of several international indexed journals (Scopus, WOS,...).

Mohamed Lahby isAssociate Professor at the Higher Normal School (ENS) University Hassan II of Casablanca, Morocco. He was awarded a PhD in Computer Science from Faculty of Sciences and Technology of Mohammedia, University Hassan II of Casablanca, in 2013. His research interests are wireless communication and network, mobility management, QoS/QoE, Internet of things, Smart cities, Optimization and Machine learning. He has published more than 50 papers (book chapters, international journals, and conferences), 7 edited books, and 2 authored book. He has served and continues to serve on executive and technical program committees of numerous international conferences such as IEEE PIMRC, ICC, NTMS, IWCMC, WINCOM, ISNCC. He also serves as a referee of many prestigious Elsevier journals : Ad Hoc Networks, Applied Computing and Informatics and International journal of disaster risk reduction. He organized and participated in more than 40 conferences and workshops. He is the chair of many international workshops and special sessions such as MLNGSN'19, CSPSC'19, MLNGSN'20, MLNGSN'21, AI2SC '20 and WCTCP'20, CIOT'22. He has also edited many books published in Springer and Taylor.

Dumitru Baleanu is a Professor at the Institute of Space Sciences, Magurele-Bucharest, Romania and a visiting staff member at the Department of Mathematics,Cankaya University, Ankara, Turkey. Dumitru got his PhD from the Institute of Atomic Physics in 1996. His fields of interest include the fractional dynamics and its applications, fractional differential equations and their applications, discrete mathematics, image processing, bio-informatics, mathematical biology, mathematical physics, soliton theory, Lie symmetry, dynamic systems on time scales, computational complexity, the wavelet method and its applications, Dumitru is a pioneer of the fractional variational principles and their applications in control theory. He is one of the co-authors of the seminal paper entitled Anomalous diffusion expressed through fractional order differential operators in the Bloch-Torrey equation, published in Journal of Magnetic Resonance (2008),which plays now a fundamental role within diffusion weighted MRI. Dumitru had an important role in developing the non-singular operators with Mittag-Leffler kernels and their applications in real world phenomena. He is a co-author of 15 books and he published more than 1000 papers indexed in ISI journals. His H index is 61 and he is a highly cited researcher in Mathematics and Engineering in 2019. He organized several prestigious international conferences in various countries. He won the ICFDA2018 Award: Innovation in Fractional Calculus and 2019- Obada Prize. Dumitru is a co-author of a Chinese Patent No: ZL 2014 1 0033835.7 regarding chaotic maps and its important role in information encryption. He is the Editor in Chief of Progress in Fractional D

Inhalt

Part. 1. Mathematical Modeling and analysis for Covid-19 Pandemic.- Chapter. 1. An Extended Fractional SEIR Model to Predict the Spreading Behavior of COVID-19 Disease using Monte-Carlo Back Sampling.- Chapter. 2. Dynamics and optimal control methods for the COVID-19 model.- Chapter. 3. Optimal Strategies to Prevent COVID-19 from Becoming a Pandemic.- Chapter. 4. Modeling and analysis of COVID-19 based on a deterministic compartmental model and Bayesian inference.- Chapter. 5. Predicting the Infection Level of Covid-19 Virus using Normal Distribution Based Approximation Model and PSO.- Chapter. 6. An Optimal Vaccination Scenario for COVID-19 Transmission Between Children and Adults.- Part. 2. Intelligent Control Techniques and Covid-19 Pandemic.- Chapter. 7. The Role of Artificial Intelligence and Machine Learning for the Fight Against COVID-19.- Chapter. 8. Coronavirus Lung Image Classification with Uncertainty Estimation using Bayesian Convolutional Neural Networks.- Chapter.9. Identify Unfavorable COVID Medicine Reactions From The Three-Dimensional Structure By Employing Convolutional Neural Network.- Chapter. 10. Using Reinforcement Learning for optimizing COVID-19 vaccine distribution strategies.- Chapter. 11. Incorporating Contextual Information and Feature Fuzzification for Effective Personalized Healthcare Recommender System.- Chapter. 12. Prediction of Growth and Review of Factors influencing the Transmission of COVID-19.- Chapter. 13. COVID-19 Combating Strategies and Associated Variables for its Transmission: An approach with multi-criteria decision-making techniques in the Indian context.- Chapter. 14. Crisis management, Internet and AI: Information in the age of COVID-19, and future pandemics.